Rating VQ and Nasalisation Pharyngeals
Jalal Al-Tamimi
Newcastle University09 June 2018
1 Introduction
This analysis accompanies the article “Khattab, G., Al-Tamimi, J., and Al-Siraih, W., (2018). Nasalisation in the production of Iraqi Arabic pharyngeals. Phonetica. DOI: https://10.1159/000487806”. Below is an analysis of the perceptual experiment that was run to evaluate raters impressions of voice quality changes and/or nasalisation that was present within pharyngeals in Iraqi Arabic.
2 Material
Data were recorded from nine Iraqi Arabic speakers producing a list of items containing the two pharyngeal consonants /ħ ʕ/ and either an oral context (i.e., oral consonant), a nasal context (i.e., nasal consonant), or only two pharyngeals (/ʕ-ʕ/). The “control” contexts were oral-oral, oral-nasal, nasal-oral, nasal-nasal, and Isolation (which was all vowels in the items produced in isolation). The items were in a CVC environment.
Our aim in this analysis was to evaluate the impression of harsh/tense voice quality and/or nasalisation in the pharyngeal contexts, as compared with all other contexts specified above was perceptually salient or not. For this, six phonetically trained raters (including the first two authors) rated these items using Praat’s MFC experiment. The data used here were a subset of the data (around 20% of the full dataset). The first author rated all items from the full dataset and the results obtained from the full set and the subset are comparable. We ran this as a rating experiment, with three levels rating for Voice Quality (experiment 1) and five levels rating for Nasalisation (experiment 2). For more details, see the article.
3 Statistical analyses
The results of the rating experiment were analysed using an Inter-Rater Reliability test and then using a Cumulative Logit Mixed Models. The aim of these two analsyes is to shed light into how pharyngeals, when in a nasal context show clear patterns of a nasal context.
3.1 Inter-Rater Reliability
We started by evaluating Inter-Rater Reliability (IRR) using Intra-Class Correlations (ICC) on each of the Voice Quality and the Nasalisation rating experiments. We were interested in both consistency and agreement between the six rater. Results in the article are reported only for the agreement. Here we present and discuss both.
3.1.1 Loading required packages
requiredPackages = c('dplyr','irr','reshape2')
for(p in requiredPackages){
if(!require(p,character.only = TRUE)) install.packages(p)
library(p,character.only = TRUE)
}3.1.2 Reading in data
We read in the data and check the structure
pharyngealsVQIRR <- read.csv("pharyngealsVQIRR.csv")
pharyngealsNasIRR <- read.csv("pharyngealsNasIRR.csv")
str(pharyngealsVQIRR)'data.frame': 2430 obs. of 4 variables:
$ subjectNew : Factor w/ 9 levels "p01","p02","p03",..: 4 4 4 4 4 4 4 4 4 4 ...
$ wordTarget : Factor w/ 45 levels "3aaf-w","3aam-w",..: 38 37 24 23 41 40 8 7 27 19 ...
$ raterNumber : Factor w/ 6 levels "R01","R02","R03",..: 2 2 2 2 2 2 2 2 2 2 ...
$ responseNumb: int 3 2 3 3 3 2 3 3 3 3 ...str(pharyngealsNasIRR)'data.frame': 2430 obs. of 4 variables:
$ subjectNew : Factor w/ 9 levels "p01","p02","p03",..: 4 4 4 4 4 4 4 4 4 4 ...
$ wordTarget : Factor w/ 45 levels "3aaf-w","3aam-w",..: 38 37 24 23 41 40 8 7 27 19 ...
$ raterNumber : Factor w/ 6 levels "R01","R02","R03",..: 2 2 2 2 2 2 2 2 2 2 ...
$ responseNumb: int 4 4 4 3 3 4 2 3 2 4 ...3.1.3 Melting and casting the data
We change a few points in the data-frame. We transform the responses into numbers. We then melt and decast the data to have it in a wide format. The new structure has 9 rows (representing data from each producing subject) and 270 columns with the ratings from each word and rater ID.
## change response into a number
pharyngealsVQIRR$responseNumb <- as.numeric(pharyngealsVQIRR$responseNumb)
pharyngealsNasIRR$responseNumb <- as.numeric(pharyngealsNasIRR$responseNumb)
## casting and melting the data frame, VQ first
pharyngealsVQIRRCast <- dcast(melt(pharyngealsVQIRR, id.vars=c("subjectNew", "wordTarget","raterNumber")), subjectNew~wordTarget+raterNumber)
## casting and melting the data frame, nasalisation second
pharyngealsNasIRRCast <- dcast(melt(pharyngealsNasIRR, id.vars=c("subjectNew", "wordTarget","raterNumber")), subjectNew~wordTarget+raterNumber)
str(pharyngealsVQIRRCast)'data.frame': 9 obs. of 271 variables:
$ subjectNew : Factor w/ 9 levels "p01","p02","p03",..: 1 2 3 4 5 6 7 8 9
$ 3aaf-w_R01 : num 3 3 3 2 3 3 2 2 3
$ 3aaf-w_R02 : num 3 3 3 3 3 3 3 3 3
$ 3aaf-w_R03 : num 2 2 2 2 3 2 2 2 3
$ 3aaf-w_R04 : num 3 2 3 1 3 1 3 3 3
$ 3aaf-w_R05 : num 2 3 3 3 3 3 3 3 3
$ 3aaf-w_R06 : num 3 3 3 3 3 3 3 2 3
$ 3aam-w_R01 : num 3 2 3 2 3 3 2 3 2
$ 3aam-w_R02 : num 3 3 3 3 3 3 3 3 3
$ 3aam-w_R03 : num 2 2 2 2 3 2 2 2 2
$ 3aam-w_R04 : num 3 2 3 2 3 1 2 3 2
$ 3aam-w_R05 : num 2 3 3 3 3 2 3 3 2
$ 3aam-w_R06 : num 2 2 2 2 2 2 2 3 2
$ 3eeb-v_R01 : num 2 2 2 1 2 2 2 2 2
$ 3eeb-v_R02 : num 2 3 2 2 2 2 3 3 2
$ 3eeb-v_R03 : num 2 2 2 2 2 2 2 2 2
$ 3eeb-v_R04 : num 2 3 2 1 2 1 2 1 2
$ 3eeb-v_R05 : num 3 3 3 3 3 1 3 3 2
$ 3eeb-v_R06 : num 2 3 3 3 2 3 2 3 2
$ 3eeb-w_R01 : num 3 3 3 2 3 2 3 2 2
$ 3eeb-w_R02 : num 3 3 3 3 3 2 3 3 2
$ 3eeb-w_R03 : num 3 3 3 2 2 2 3 2 2
$ 3eeb-w_R04 : num 3 3 3 3 2 2 3 2 2
$ 3eeb-w_R05 : num 3 3 3 3 2 3 3 3 2
$ 3eeb-w_R06 : num 2 3 3 3 2 2 2 3 2
$ 3een-w_R01 : num 3 2 2 2 3 2 2 3 3
$ 3een-w_R02 : num 3 3 3 3 3 3 2 3 3
$ 3een-w_R03 : num 3 3 2 2 2 2 2 3 3
$ 3een-w_R04 : num 3 2 2 2 3 2 2 3 2
$ 3een-w_R05 : num 2 3 3 3 2 3 2 3 3
$ 3een-w_R06 : num 2 2 3 3 3 2 2 2 2
$ 3iish-w_R01: num 3 3 3 3 3 3 1 3 3
$ 3iish-w_R02: num 3 3 3 3 3 3 2 3 3
$ 3iish-w_R03: num 3 3 3 3 3 3 2 3 3
$ 3iish-w_R04: num 3 2 3 2 2 2 3 1 2
$ 3iish-w_R05: num 2 3 3 3 3 3 2 3 3
$ 3iish-w_R06: num 2 3 3 3 3 3 2 2 3
$ 3oo3-v_R01 : num 2 2 2 2 2 2 2 2 2
$ 3oo3-v_R02 : num 2 3 2 3 3 2 3 2 2
$ 3oo3-v_R03 : num 2 2 2 2 2 2 2 2 2
$ 3oo3-v_R04 : num 2 2 2 2 1 1 2 1 2
$ 3oo3-v_R05 : num 3 3 3 1 3 2 2 1 3
$ 3oo3-v_R06 : num 2 2 2 3 2 2 3 2 2
$ 3oo3-w_R01 : num 3 3 3 3 3 3 3 1 3
$ 3oo3-w_R02 : num 3 3 3 3 3 3 3 3 3
$ 3oo3-w_R03 : num 2 2 2 2 3 2 3 2 3
$ 3oo3-w_R04 : num 3 3 3 2 3 1 3 2 3
$ 3oo3-w_R05 : num 3 3 3 2 2 3 3 2 3
$ 3oo3-w_R06 : num 3 3 3 2 3 3 3 2 2
$ 3oon-w_R01 : num 3 3 3 2 3 3 2 3 1
$ 3oon-w_R02 : num 3 2 3 2 3 2 2 3 3
$ 3oon-w_R03 : num 3 2 3 2 3 2 2 3 3
$ 3oon-w_R04 : num 3 2 2 2 3 2 2 3 3
$ 3oon-w_R05 : num 2 3 3 2 1 2 2 3 2
$ 3oon-w_R06 : num 1 3 3 2 3 2 2 2 3
$ 7aad-w_R01 : num 3 2 3 3 3 2 2 3 3
$ 7aad-w_R02 : num 3 3 3 3 3 3 3 3 3
$ 7aad-w_R03 : num 2 2 3 3 2 2 2 3 3
$ 7aad-w_R04 : num 3 2 3 2 3 3 2 3 2
$ 7aad-w_R05 : num 2 3 3 3 2 2 3 3 2
$ 7aad-w_R06 : num 2 3 3 2 2 3 2 2 2
$ 7eef-v_R01 : num 2 1 1 2 2 2 2 2 2
$ 7eef-v_R02 : num 2 3 2 2 2 2 2 3 2
$ 7eef-v_R03 : num 2 2 2 2 2 2 2 2 2
$ 7eef-v_R04 : num 2 1 2 1 2 2 3 2 2
$ 7eef-v_R05 : num 1 3 2 3 3 2 1 3 2
$ 7eef-v_R06 : num 1 3 3 1 2 2 3 2 2
$ 7eef-w_R01 : num 2 2 2 2 2 2 2 3 2
$ 7eef-w_R02 : num 3 3 3 3 3 2 3 3 2
$ 7eef-w_R03 : num 2 2 2 2 2 2 2 2 1
$ 7eef-w_R04 : num 3 2 2 2 1 2 2 3 3
$ 7eef-w_R05 : num 2 2 3 2 2 2 3 3 3
$ 7eef-w_R06 : num 2 3 3 1 3 2 2 2 3
$ 7en-w_R01 : num 3 2 2 2 3 2 2 2 2
$ 7en-w_R02 : num 3 3 3 2 3 2 3 3 3
$ 7en-w_R03 : num 2 2 2 2 3 2 2 3 3
$ 7en-w_R04 : num 3 3 3 2 2 1 2 3 3
$ 7en-w_R05 : num 3 3 3 2 2 2 2 3 2
$ 7en-w_R06 : num 2 3 3 2 3 2 2 2 2
$ 7ook-w_R01 : num 3 2 2 2 3 2 2 3 2
$ 7ook-w_R02 : num 3 3 3 2 3 2 3 3 3
$ 7ook-w_R03 : num 2 3 2 2 3 2 2 2 1
$ 7ook-w_R04 : num 2 2 3 2 3 1 2 2 2
$ 7ook-w_R05 : num 1 2 3 2 2 2 3 2 1
$ 7ook-w_R06 : num 2 3 3 2 3 2 2 2 1
$ 7oom-w_R01 : num 3 2 2 1 3 2 1 3 1
$ 7oom-w_R02 : num 3 3 3 2 3 2 3 3 3
$ 7oom-w_R03 : num 2 2 2 2 2 2 2 3 1
$ 7oom-w_R04 : num 2 2 2 1 3 1 2 3 2
$ 7oom-w_R05 : num 1 2 3 1 2 2 3 3 1
$ 7oom-w_R06 : num 2 2 2 3 3 2 3 2 1
$ baa3-w_R01 : num 2 2 3 3 3 2 2 2 2
$ baa3-w_R02 : num 3 3 3 3 3 3 3 2 2
$ baa3-w_R03 : num 2 2 2 3 2 3 2 2 2
$ baa3-w_R04 : num 2 2 2 2 3 3 2 2 2
$ baa3-w_R05 : num 3 3 3 3 3 3 3 3 3
$ baa3-w_R06 : num 2 2 3 2 3 2 2 2 2
$ beet-w_R01 : num 2 2 1 2 2 2 2 2 2
$ beet-w_R02 : num 2 2 2 2 2 2 2 2 2
[list output truncated]3.1.4 Intra-Class Correlations (ICC)
We used ICCs to compute inter-rater reliability. For details see the article. We ran two types of tests. We wanted to check how consistent the raters were in their choices and also how they agree between each other. The agreements were reported in the article. We start by reporting the overall consistency and agreement levels for each of the VQ and Nasalisation experiments and then on the consistency and agreement on each word, i.e., an Item-based analysis.
3.1.4.1 Voice Quality (VQ)
We start with the VQ experiment.
3.1.4.1.1 Consistency
Below are the model specifications for the consistency.
VQICCRatingConsWordAver <- icc(pharyngealsVQIRRCast[2:271],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver3aafw <- icc(pharyngealsVQIRRCast[2:7],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver3aamw <- icc(pharyngealsVQIRRCast[8:13],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver3eebv <- icc(pharyngealsVQIRRCast[14:19],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver3eebw <- icc(pharyngealsVQIRRCast[20:25],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver3eenw <- icc(pharyngealsVQIRRCast[26:31],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver3iishw <- icc(pharyngealsVQIRRCast[32:37],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver3oo3v <- icc(pharyngealsVQIRRCast[38:43],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver3oo3w <- icc(pharyngealsVQIRRCast[44:49],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver3oonw <- icc(pharyngealsVQIRRCast[50:55],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver7aadw <- icc(pharyngealsVQIRRCast[56:61],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver7eefv <- icc(pharyngealsVQIRRCast[62:67],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver7eefw <- icc(pharyngealsVQIRRCast[68:73],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver7enw <- icc(pharyngealsVQIRRCast[74:79],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver7ookw <- icc(pharyngealsVQIRRCast[80:85],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver7oomw <- icc(pharyngealsVQIRRCast[86:91],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverbaa3w <- icc(pharyngealsVQIRRCast[92:97],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverbeetw <- icc(pharyngealsVQIRRCast[98:103],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverbii3v <- icc(pharyngealsVQIRRCast[104:109],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverbii3w <- icc(pharyngealsVQIRRCast[110:115],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverbooshw <- icc(pharyngealsVQIRRCast[116:121],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverdaasw <- icc(pharyngealsVQIRRCast[122:127],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverdiinw <- icc(pharyngealsVQIRRCast[128:133],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverdjuu3v <- icc(pharyngealsVQIRRCast[134:139],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverdjuu3w <- icc(pharyngealsVQIRRCast[140:145],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverdoomv <- icc(pharyngealsVQIRRCast[146:151],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverdoomw <- icc(pharyngealsVQIRRCast[152:157],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverloo7w <- icc(pharyngealsVQIRRCast[158:163],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvermaatv <- icc(pharyngealsVQIRRCast[164:169],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvermaatw <- icc(pharyngealsVQIRRCast[170:175],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvermoozw <- icc(pharyngealsVQIRRCast[176:181],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvermuunw <- icc(pharyngealsVQIRRCast[182:187],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvermuusw <- icc(pharyngealsVQIRRCast[188:193],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvernaa7v <- icc(pharyngealsVQIRRCast[194:199],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvernaa7w <- icc(pharyngealsVQIRRCast[200:205],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvernaamv <- icc(pharyngealsVQIRRCast[206:211],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvernaamw <- icc(pharyngealsVQIRRCast[212:217],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvernoo3v <- icc(pharyngealsVQIRRCast[218:223],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvernoo3w <- icc(pharyngealsVQIRRCast[224:229],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvernoomw <- icc(pharyngealsVQIRRCast[230:235],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvernuu7v <- icc(pharyngealsVQIRRCast[236:241],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvernuu7w <- icc(pharyngealsVQIRRCast[242:247],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAversaamv <- icc(pharyngealsVQIRRCast[248:253],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAversaamw <- icc(pharyngealsVQIRRCast[254:259],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverzaa7w <- icc(pharyngealsVQIRRCast[260:265],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverzii7w <- icc(pharyngealsVQIRRCast[266:271],model="twoway",type="consistency",unit="average")3.1.4.1.2 Agreement
Below are the model specifications for the agreement
VQICCRatingAgreeWordAver <- icc(pharyngealsVQIRRCast[2:271],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver3aafw <- icc(pharyngealsVQIRRCast[2:7],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver3aamw <- icc(pharyngealsVQIRRCast[8:13],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver3eebv <- icc(pharyngealsVQIRRCast[14:19],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver3eebw <- icc(pharyngealsVQIRRCast[20:25],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver3eenw <- icc(pharyngealsVQIRRCast[26:31],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver3iishw <- icc(pharyngealsVQIRRCast[32:37],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver3oo3v <- icc(pharyngealsVQIRRCast[38:43],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver3oo3w <- icc(pharyngealsVQIRRCast[44:49],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver3oonw <- icc(pharyngealsVQIRRCast[50:55],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver7aadw <- icc(pharyngealsVQIRRCast[56:61],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver7eefv <- icc(pharyngealsVQIRRCast[62:67],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver7eefw <- icc(pharyngealsVQIRRCast[68:73],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver7enw <- icc(pharyngealsVQIRRCast[74:79],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver7ookw <- icc(pharyngealsVQIRRCast[80:85],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver7oomw <- icc(pharyngealsVQIRRCast[86:91],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverbaa3w <- icc(pharyngealsVQIRRCast[92:97],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverbeetw <- icc(pharyngealsVQIRRCast[98:103],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverbii3v <- icc(pharyngealsVQIRRCast[104:109],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverbii3w <- icc(pharyngealsVQIRRCast[110:115],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverbooshw <- icc(pharyngealsVQIRRCast[116:121],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverdaasw <- icc(pharyngealsVQIRRCast[122:127],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverdiinw <- icc(pharyngealsVQIRRCast[128:133],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverdjuu3v <- icc(pharyngealsVQIRRCast[134:139],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverdjuu3w <- icc(pharyngealsVQIRRCast[140:145],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverdoomv <- icc(pharyngealsVQIRRCast[146:151],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverdoomw <- icc(pharyngealsVQIRRCast[152:157],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverloo7w <- icc(pharyngealsVQIRRCast[158:163],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvermaatv <- icc(pharyngealsVQIRRCast[164:169],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvermaatw <- icc(pharyngealsVQIRRCast[170:175],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvermoozw <- icc(pharyngealsVQIRRCast[176:181],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvermuunw <- icc(pharyngealsVQIRRCast[182:187],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvermuusw <- icc(pharyngealsVQIRRCast[188:193],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvernaa7v <- icc(pharyngealsVQIRRCast[194:199],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvernaa7w <- icc(pharyngealsVQIRRCast[200:205],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvernaamv <- icc(pharyngealsVQIRRCast[206:211],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvernaamw <- icc(pharyngealsVQIRRCast[212:217],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvernoo3v <- icc(pharyngealsVQIRRCast[218:223],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvernoo3w <- icc(pharyngealsVQIRRCast[224:229],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvernoomw <- icc(pharyngealsVQIRRCast[230:235],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvernuu7v <- icc(pharyngealsVQIRRCast[236:241],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvernuu7w <- icc(pharyngealsVQIRRCast[242:247],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAversaamv <- icc(pharyngealsVQIRRCast[248:253],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAversaamw <- icc(pharyngealsVQIRRCast[254:259],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverzaa7w <- icc(pharyngealsVQIRRCast[260:265],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverzii7w <- icc(pharyngealsVQIRRCast[266:271],model="twoway",type="agreement",unit="average")3.1.4.2 Nasalisation
We move to the nasalisation experiment.
3.1.4.2.1 Consistency
Below are the model specifications for the consistency.
NasICCRatingConsWordAver <- icc(pharyngealsNasIRRCast[2:271],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver3aafw <- icc(pharyngealsNasIRRCast[2:7],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver3aamw <- icc(pharyngealsNasIRRCast[8:13],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver3eebv <- icc(pharyngealsNasIRRCast[14:19],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver3eebw <- icc(pharyngealsNasIRRCast[20:25],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver3eenw <- icc(pharyngealsNasIRRCast[26:31],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver3iishw <- icc(pharyngealsNasIRRCast[32:37],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver3oo3v <- icc(pharyngealsNasIRRCast[38:43],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver3oo3w <- icc(pharyngealsNasIRRCast[44:49],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver3oonw <- icc(pharyngealsNasIRRCast[50:55],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver7aadw <- icc(pharyngealsNasIRRCast[56:61],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver7eefv <- icc(pharyngealsNasIRRCast[62:67],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver7eefw <- icc(pharyngealsNasIRRCast[68:73],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver7enw <- icc(pharyngealsNasIRRCast[74:79],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver7ookw <- icc(pharyngealsNasIRRCast[80:85],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver7oomw <- icc(pharyngealsNasIRRCast[86:91],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverbaa3w <- icc(pharyngealsNasIRRCast[92:97],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverbeetw <- icc(pharyngealsNasIRRCast[98:103],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverbii3v <- icc(pharyngealsNasIRRCast[104:109],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverbii3w <- icc(pharyngealsNasIRRCast[110:115],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverbooshw <- icc(pharyngealsNasIRRCast[116:121],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverdaasw <- icc(pharyngealsNasIRRCast[122:127],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverdiinw <- icc(pharyngealsNasIRRCast[128:133],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverdjuu3v <- icc(pharyngealsNasIRRCast[134:139],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverdjuu3w <- icc(pharyngealsNasIRRCast[140:145],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverdoomv <- icc(pharyngealsNasIRRCast[146:151],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverdoomw <- icc(pharyngealsNasIRRCast[152:157],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverloo7w <- icc(pharyngealsNasIRRCast[158:163],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvermaatv <- icc(pharyngealsNasIRRCast[164:169],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvermaatw <- icc(pharyngealsNasIRRCast[170:175],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvermoozw <- icc(pharyngealsNasIRRCast[176:181],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvermuunw <- icc(pharyngealsNasIRRCast[182:187],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvermuusw <- icc(pharyngealsNasIRRCast[188:193],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvernaa7v <- icc(pharyngealsNasIRRCast[194:199],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvernaa7w <- icc(pharyngealsNasIRRCast[200:205],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvernaamv <- icc(pharyngealsNasIRRCast[206:211],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvernaamw <- icc(pharyngealsNasIRRCast[212:217],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvernoo3v <- icc(pharyngealsNasIRRCast[218:223],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvernoo3w <- icc(pharyngealsNasIRRCast[224:229],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvernoomw <- icc(pharyngealsNasIRRCast[230:235],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvernuu7v <- icc(pharyngealsNasIRRCast[236:241],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvernuu7w <- icc(pharyngealsNasIRRCast[242:247],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAversaamv <- icc(pharyngealsNasIRRCast[248:253],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAversaamw <- icc(pharyngealsNasIRRCast[254:259],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverzaa7w <- icc(pharyngealsNasIRRCast[260:265],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverzii7w <- icc(pharyngealsNasIRRCast[266:271],model="twoway",type="consistency",unit="average")3.1.4.2.2 Agreement
Below are the model specifications for the agreement
NasICCRatingAgreeWordAver <- icc(pharyngealsNasIRRCast[2:271],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver3aafw <- icc(pharyngealsNasIRRCast[2:7],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver3aamw <- icc(pharyngealsNasIRRCast[8:13],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver3eebv <- icc(pharyngealsNasIRRCast[14:19],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver3eebw <- icc(pharyngealsNasIRRCast[20:25],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver3eenw <- icc(pharyngealsNasIRRCast[26:31],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver3iishw <- icc(pharyngealsNasIRRCast[32:37],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver3oo3v <- icc(pharyngealsNasIRRCast[38:43],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver3oo3w <- icc(pharyngealsNasIRRCast[44:49],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver3oonw <- icc(pharyngealsNasIRRCast[50:55],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver7aadw <- icc(pharyngealsNasIRRCast[56:61],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver7eefv <- icc(pharyngealsNasIRRCast[62:67],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver7eefw <- icc(pharyngealsNasIRRCast[68:73],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver7enw <- icc(pharyngealsNasIRRCast[74:79],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver7ookw <- icc(pharyngealsNasIRRCast[80:85],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver7oomw <- icc(pharyngealsNasIRRCast[86:91],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverbaa3w <- icc(pharyngealsNasIRRCast[92:97],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverbeetw <- icc(pharyngealsNasIRRCast[98:103],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverbii3v <- icc(pharyngealsNasIRRCast[104:109],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverbii3w <- icc(pharyngealsNasIRRCast[110:115],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverbooshw <- icc(pharyngealsNasIRRCast[116:121],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverdaasw <- icc(pharyngealsNasIRRCast[122:127],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverdiinw <- icc(pharyngealsNasIRRCast[128:133],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverdjuu3v <- icc(pharyngealsNasIRRCast[134:139],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverdjuu3w <- icc(pharyngealsNasIRRCast[140:145],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverdoomv <- icc(pharyngealsNasIRRCast[146:151],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverdoomw <- icc(pharyngealsNasIRRCast[152:157],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverloo7w <- icc(pharyngealsNasIRRCast[158:163],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvermaatv <- icc(pharyngealsNasIRRCast[164:169],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvermaatw <- icc(pharyngealsNasIRRCast[170:175],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvermoozw <- icc(pharyngealsNasIRRCast[176:181],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvermuunw <- icc(pharyngealsNasIRRCast[182:187],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvermuusw <- icc(pharyngealsNasIRRCast[188:193],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvernaa7v <- icc(pharyngealsNasIRRCast[194:199],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvernaa7w <- icc(pharyngealsNasIRRCast[200:205],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvernaamv <- icc(pharyngealsNasIRRCast[206:211],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvernaamw <- icc(pharyngealsNasIRRCast[212:217],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvernoo3v <- icc(pharyngealsNasIRRCast[218:223],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvernoo3w <- icc(pharyngealsNasIRRCast[224:229],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvernoomw <- icc(pharyngealsNasIRRCast[230:235],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvernuu7v <- icc(pharyngealsNasIRRCast[236:241],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvernuu7w <- icc(pharyngealsNasIRRCast[242:247],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAversaamv <- icc(pharyngealsNasIRRCast[248:253],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAversaamw <- icc(pharyngealsNasIRRCast[254:259],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverzaa7w <- icc(pharyngealsNasIRRCast[260:265],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverzii7w <- icc(pharyngealsNasIRRCast[266:271],model="twoway",type="agreement",unit="average")3.1.5 Getting coefficients
Below, we use a a modified script that will allow us to get all coefficients from the models (thanks to Bodo Winter for sharing).
###########################################
#getting coefficients # Adapted from Bodo Winter
###########################################
## Get character vectors with all model names for full and null models:
all_models <- grep('ICCRating', ls(), value = T)
## Create an empty data frame to be filled with results from likelihood ratio tests:
resultsICC <- data.frame(Type = character(length(all_models)),
ICCVal = numeric(length(all_models)),
lowerConfInt = numeric(length(all_models)),
upperConfInt = numeric(length(all_models)),
pValue = numeric(length(all_models)),
stringsAsFactors=FALSE)
## Loop through model names and append do data frame:
for (i in 1:length(all_models)) {
this_full <- get(all_models[i])
resultsICC[i, ]$Type <- this_full$type
resultsICC[i, ]$ICCVal <- this_full$value
resultsICC[i, ]$lowerConfInt <- this_full$lbound
resultsICC[i, ]$upperConfInt <- this_full$ubound
resultsICC[i, ]$pValue <- this_full$p.value
}
rownames(resultsICC) <- gsub('ICCRating', 'ICCRating', all_models)
write.csv(resultsICC, file = "PhoneticaResultsICCRating.csv")3.1.6 Reading and subsetting the ICC data
We have done some minor changes to the saved data-frame in terms of names, etc.. and so we read the data-frame again. We start by subsetting the data and creating four new data-frames for each of the VQ and Nasalisation experiments and the consistency and agreement. We then change factor levels.
PhoneticaICCRatingWord <- read.csv("PhoneticaICCRatingWord.csv")
# some subsetting
PhoneticaICCRatingWordNasAgree <- PhoneticaICCRatingWord[which(PhoneticaICCRatingWord$experiment =='Nasalisation'
& PhoneticaICCRatingWord$Type == 'agreement'),]
PhoneticaICCRatingWordNasCons <- PhoneticaICCRatingWord[which(PhoneticaICCRatingWord$experiment =='Nasalisation'
& PhoneticaICCRatingWord$Type == 'consistency'),]
PhoneticaICCRatingWordVQAgree <- PhoneticaICCRatingWord[which(PhoneticaICCRatingWord$experiment =='Voice Quality'
& PhoneticaICCRatingWord$Type == 'agreement'),]
PhoneticaICCRatingWordVQCons <- PhoneticaICCRatingWord[which(PhoneticaICCRatingWord$experiment =='Voice Quality'
& PhoneticaICCRatingWord$Type == 'consistency'),]
### below is to be used to change levels of factor after subsetting
PhoneticaICCRatingWordNasAgree$experiment <- factor(PhoneticaICCRatingWordNasAgree$experiment)
PhoneticaICCRatingWordNasAgree$Type <- factor(PhoneticaICCRatingWordNasAgree$Type)
PhoneticaICCRatingWordNasCons$experiment <- factor(PhoneticaICCRatingWordNasCons$experiment)
PhoneticaICCRatingWordNasCons$Type <- factor(PhoneticaICCRatingWordNasCons$Type)
PhoneticaICCRatingWordVQAgree$experiment <- factor(PhoneticaICCRatingWordVQAgree$experiment)
PhoneticaICCRatingWordVQAgree$Type <- factor(PhoneticaICCRatingWordVQAgree$Type)
PhoneticaICCRatingWordVQCons$experiment <- factor(PhoneticaICCRatingWordVQCons$experiment)
PhoneticaICCRatingWordVQCons$Type <- factor(PhoneticaICCRatingWordVQCons$Type)3.1.7 Figures
Below we change the order of the four data-frame according to the ICC value from lowest to highest. We then reorder the factor level “context”. Then we change the name of each of the words into IPA (International Phonetic Alphabet). We create a new ordered factor and then we draw the figure.
3.1.7.1 Voice Quality (VQ)
We start with the first experiment on Voice Quality. Overall, the raters reported difficulties with their ratings of the voice quality experiment, mostly in deciding on the third category “tense”. This showed in their consistency and agreements
3.1.7.1.1 Consistency
We start with the consistency
3.1.7.1.1.1 Reordering and changing names
## Consistency voice quality
ord.reRatingConsVQ <- PhoneticaICCRatingWordVQCons[order(PhoneticaICCRatingWordVQCons$ICCVal),]
rownames(ord.reRatingConsVQ) <- NULL
ord.reRatingConsVQ$context <- factor(ord.reRatingConsVQ$context)
ord.reRatingConsVQ$context <- as.character(ord.reRatingConsVQ$context)
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "7aad-w"] <- "\u0127a:d"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "7eef-w"] <- "\u0127e:f"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "7eef-v"] <- "\u0127e:f-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "7en-w"] <- "\u0127enn"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "7ook-w"] <- "\u0127o:k"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "7oom-w"] <- "\u0127o:m"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "loo7-w"] <- "lo:\u0127"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "naa7-w"] <- "na:\u0127"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "naa7-v"] <- "na:\u0127-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "nuu7-w"] <- "nu:\u0127"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "nuu7-v"] <- "nu:\u0127-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "zaa7-w"] <- "za:\u0127"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "zii7-w"] <- "zi:\u0127"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "3aaf-w"] <- "ʕa:f"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "3aam-w"] <- "ʕa:m"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "3eeb-w"] <- "ʕe:b"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "3eeb-v"] <- "ʕe:b-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "3een-w"] <- "ʕe:n"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "3iish-w"] <- "ʕa:ʃ"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "3oo3-w"] <- "ʕo:ʕ"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "3oo3-v"] <- "ʕo:ʕ-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "3oon-w"] <- "ʕo:n"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "baa3-w"] <- "ba:ʕ"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "bii3-w"] <- "bi:ʕ"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "bii3-v"] <- "bi:ʕ-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "boosh-w"] <- "bo:ʃ"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "djuu3-w"] <- "d͡ʒu:ʕ"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "djuu3-v"] <- "d͡ʒu:ʕ-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "noo3-w"] <- "no:ʕ"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "noo3-v"] <- "no:ʕ-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "beet-w"] <- "be:t"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "daas-w"] <- "da:s"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "diin-w"] <- "di:n"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "doom-w"] <- "do:m"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "doom-v"] <- "do:m-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "beet-w"] <- "be:t"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "maat-w"] <- "ma:t"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "maat-v"] <- "ma:t-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "mooz-w"] <- "mo:z"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "muun-w"] <- "mu:n"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "muus-w"] <- "mu:s"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "naam-w"] <- "na:m"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "naam-v"] <- "na:m-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "noom-w"] <- "no:m"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "saam-w"] <- "sa:m"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "saam-v"] <- "sa:m-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "beet-w"] <- "be:t"
ord.reRatingConsVQ$context <- as.factor(ord.reRatingConsVQ$context)
#
ord.reRatingConsVQ$context <- as.vector(ord.reRatingConsVQ$context) #get rid of factors
ord.reRatingConsVQ$context = factor(ord.reRatingConsVQ$context,ord.reRatingConsVQ$context) #add ordered factors back
ord.reRatingConsVQ3.1.7.1.1.2 Drawing the IRR figure
We use the code below to draw the ICC with confidence intervals for the consistency in rating voice quality.
## code below allows to draw the figure
par(oma = c(0, 0, 0, 0))
plot(1:45, ord.reRatingConsVQ$ICCVal, axes=FALSE, ylim=c(-3,1),
ylab="ICC. 95% CI", xlab="",cex=2,cex.lab=1.5,cex.main=1.5,
main="Consistency ICC for voice quality",cex.axis=1.5)
axis(2,cex.lab=2,cex.axis=1.5)axis(1,at=1:45, labels = levels(ord.reRatingConsVQ$context),
las=2,cex.lab=1.5,cex.axis=1.5)
for(i in 1:45) segments(i,ord.reRatingConsVQ$lowerConfInt[i],
i,ord.reRatingConsVQ$upperConfInt[i])abline(h = 0, lty=2)
abline(v = 32.5, lty=2)
As can be seen from the figure above, the rater were generally consistent in their ratings as all the words, minus the first 6 received positive ratings. The ratings for the last 12 words showed a consistently statistically significant positive rating.
3.1.7.1.2 Agreement
We move to the agreement between raters
3.1.7.1.2.1 Reordering and changing names
## Agreement Voice Quality
ord.reRatingAgreeVQ <- PhoneticaICCRatingWordVQAgree[order(PhoneticaICCRatingWordVQAgree$ICCVal),]
rownames(ord.reRatingAgreeVQ) <- NULL
ord.reRatingAgreeVQ$context <- factor(ord.reRatingAgreeVQ$context)
ord.reRatingAgreeVQ$context <- as.character(ord.reRatingAgreeVQ$context)
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "7aad-w"] <- "\u0127a:d"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "7eef-w"] <- "\u0127e:f"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "7eef-v"] <- "\u0127e:f-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "7en-w"] <- "\u0127enn"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "7ook-w"] <- "\u0127o:k"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "7oom-w"] <- "\u0127o:m"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "loo7-w"] <- "lo:\u0127"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "naa7-w"] <- "na:\u0127"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "naa7-v"] <- "na:\u0127-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "nuu7-w"] <- "nu:\u0127"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "nuu7-v"] <- "nu:\u0127-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "zaa7-w"] <- "za:\u0127"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "zii7-w"] <- "zi:\u0127"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "3aaf-w"] <- "ʕa:f"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "3aam-w"] <- "ʕa:m"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "3eeb-w"] <- "ʕe:b"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "3eeb-v"] <- "ʕe:b-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "3een-w"] <- "ʕe:n"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "3iish-w"] <- "ʕa:ʃ"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "3oo3-w"] <- "ʕo:ʕ"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "3oo3-v"] <- "ʕo:ʕ-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "3oon-w"] <- "ʕo:n"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "baa3-w"] <- "ba:ʕ"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "bii3-w"] <- "bi:ʕ"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "bii3-v"] <- "bi:ʕ-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "boosh-w"] <- "bo:ʃ"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "djuu3-w"] <- "d͡ʒu:ʕ"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "djuu3-v"] <- "d͡ʒu:ʕ-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "noo3-w"] <- "no:ʕ"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "noo3-v"] <- "no:ʕ-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "beet-w"] <- "be:t"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "daas-w"] <- "da:s"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "diin-w"] <- "di:n"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "doom-w"] <- "do:m"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "doom-v"] <- "do:m-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "beet-w"] <- "be:t"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "maat-w"] <- "ma:t"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "maat-v"] <- "ma:t-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "mooz-w"] <- "mo:z"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "muun-w"] <- "mu:n"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "muus-w"] <- "mu:s"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "naam-w"] <- "na:m"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "naam-v"] <- "na:m-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "noom-w"] <- "no:m"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "saam-w"] <- "sa:m"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "saam-v"] <- "sa:m-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "beet-w"] <- "be:t"
ord.reRatingAgreeVQ$context <- as.factor(ord.reRatingAgreeVQ$context)
ord.reRatingAgreeVQ$context <- as.vector(ord.reRatingAgreeVQ$context) #get rid of factors
ord.reRatingAgreeVQ$context = factor(ord.reRatingAgreeVQ$context,ord.reRatingAgreeVQ$context) #add ordered factors back
ord.reRatingAgreeVQ3.1.7.1.2.2 Drawing the IRR figure
plot(1:45, ord.reRatingAgreeVQ$ICCVal, axes=FALSE, ylim=c(-1,1),
ylab="ICC. 95% CI", xlab="",cex=2,cex.lab=1.5,cex.main=1.5,
main="Agreement ICC for voice quality",cex.axis=1.5)
axis(2,cex.lab=2,cex.axis=1.5)axis(1,at=1:45, labels = levels(ord.reRatingAgreeVQ$context),
las=2,cex.lab=1.5,cex.axis=1.5)
for(i in 1:45) segments(i,ord.reRatingAgreeVQ$lowerConfInt[i],
i,ord.reRatingAgreeVQ$upperConfInt[i])abline(h = 0, lty=2)
abline(v = 33.5, lty=2)
As with the consistency above, the raters agreed among themselves on the majority of words as these received positive ICCs. The last 12 again received an agreement in ratings that was statistically significant. Some words containing pharyngeals, and specifically the voiced pharyngeal, receive positive ratings.
3.1.7.2 Nasalisation
We move to the second experiment on Nasalisation. Overall, this experiment was slightly easier for the raters as evidenced by the consistency and agreement in their ICC scores.
3.1.7.2.1 Consistency
We start with the consistency
3.1.7.2.1.1 Reordering and changing names
## Consistency nasalisation
ord.reRatingConsNas <- PhoneticaICCRatingWordNasCons[order(PhoneticaICCRatingWordNasCons$ICCVal),]
rownames(ord.reRatingConsNas) <- NULL
ord.reRatingConsNas$context <- factor(ord.reRatingConsNas$context)
ord.reRatingConsNas$context <- as.character(ord.reRatingConsNas$context)
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "7aad-w"] <- "\u0127a:d"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "7eef-w"] <- "\u0127e:f"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "7eef-v"] <- "\u0127e:f-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "7en-w"] <- "\u0127enn"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "7ook-w"] <- "\u0127o:k"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "7oom-w"] <- "\u0127o:m"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "loo7-w"] <- "lo:\u0127"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "naa7-w"] <- "na:\u0127"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "naa7-v"] <- "na:\u0127-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "nuu7-w"] <- "nu:\u0127"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "nuu7-v"] <- "nu:\u0127-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "zaa7-w"] <- "za:\u0127"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "zii7-w"] <- "zi:\u0127"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "3aaf-w"] <- "ʕa:f"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "3aam-w"] <- "ʕa:m"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "3eeb-w"] <- "ʕe:b"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "3eeb-v"] <- "ʕe:b-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "3een-w"] <- "ʕe:n"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "3iish-w"] <- "ʕa:ʃ"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "3oo3-w"] <- "ʕo:ʕ"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "3oo3-v"] <- "ʕo:ʕ-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "3oon-w"] <- "ʕo:n"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "baa3-w"] <- "ba:ʕ"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "bii3-w"] <- "bi:ʕ"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "bii3-v"] <- "bi:ʕ-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "boosh-w"] <- "bo:ʃ"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "djuu3-w"] <- "d͡ʒu:ʕ"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "djuu3-v"] <- "d͡ʒu:ʕ-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "noo3-w"] <- "no:ʕ"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "noo3-v"] <- "no:ʕ-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "beet-w"] <- "be:t"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "daas-w"] <- "da:s"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "diin-w"] <- "di:n"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "doom-w"] <- "do:m"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "doom-v"] <- "do:m-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "beet-w"] <- "be:t"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "maat-w"] <- "ma:t"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "maat-v"] <- "ma:t-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "mooz-w"] <- "mo:z"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "muun-w"] <- "mu:n"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "muus-w"] <- "mu:s"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "naam-w"] <- "na:m"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "naam-v"] <- "na:m-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "noom-w"] <- "no:m"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "saam-w"] <- "sa:m"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "saam-v"] <- "sa:m-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "beet-w"] <- "be:t"
ord.reRatingConsNas$context <- as.factor(ord.reRatingConsNas$context)
ord.reRatingConsNas$context <- as.vector(ord.reRatingConsNas$context) #get rid of factors
ord.reRatingConsNas$context = factor(ord.reRatingConsNas$context,ord.reRatingConsNas$context) #add ordered factors back
ord.reRatingConsNas3.1.7.2.1.2 Drawing the IRR figure
par(oma = c(0, 0, 0, 0))
plot(1:45, ord.reRatingConsNas$ICCVal, axes=FALSE, ylim=c(-1,1),
ylab="ICC. 95% CI", xlab="",cex=2,cex.lab=1.5,cex.main=1.5,
main="Consistency ICC for nasalisation",cex.axis=1.5)
axis(2,cex.lab=2,cex.axis=1.5)axis(1,at=1:45, labels = levels(ord.reRatingConsNas$context),
las=2,cex.lab=1.5,cex.axis=1.5)
for(i in 1:45) segments(i,ord.reRatingConsNas$lowerConfInt[i],
i,ord.reRatingConsNas$upperConfInt[i])abline(h = 0, lty=2)
abline(v = 21.5, lty=2)
The nasalisation experiment was somehow “easier” to raters as they were always consistent in their ratings with only two words received negative ICCs and nearly half of the words showing a statistically significant positive rating.
3.1.7.2.2 Agreement
We move to the agreement
3.1.7.2.2.1 Reordering and changing names
## Agreement nasalisation
ord.reRatingAgreeNas <- PhoneticaICCRatingWordNasAgree[order(PhoneticaICCRatingWordNasAgree$ICCVal),]
rownames(ord.reRatingAgreeNas) <- NULL
ord.reRatingAgreeNas$context <- factor(ord.reRatingAgreeNas$context)
ord.reRatingAgreeNas$context <- as.character(ord.reRatingAgreeNas$context)
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "7aad-w"] <- "\u0127a:d"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "7eef-w"] <- "\u0127e:f"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "7eef-v"] <- "\u0127e:f-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "7en-w"] <- "\u0127enn"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "7ook-w"] <- "\u0127o:k"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "7oom-w"] <- "\u0127o:m"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "loo7-w"] <- "lo:\u0127"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "naa7-w"] <- "na:\u0127"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "naa7-v"] <- "na:\u0127-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "nuu7-w"] <- "nu:\u0127"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "nuu7-v"] <- "nu:\u0127-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "zaa7-w"] <- "za:\u0127"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "zii7-w"] <- "zi:\u0127"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "3aaf-w"] <- "ʕa:f"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "3aam-w"] <- "ʕa:m"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "3eeb-w"] <- "ʕe:b"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "3eeb-v"] <- "ʕe:b-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "3een-w"] <- "ʕe:n"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "3iish-w"] <- "ʕa:ʃ"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "3oo3-w"] <- "ʕo:ʕ"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "3oo3-v"] <- "ʕo:ʕ-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "3oon-w"] <- "ʕo:n"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "baa3-w"] <- "ba:ʕ"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "bii3-w"] <- "bi:ʕ"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "bii3-v"] <- "bi:ʕ-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "boosh-w"] <- "bo:ʃ"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "djuu3-w"] <- "d͡ʒu:ʕ"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "djuu3-v"] <- "d͡ʒu:ʕ-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "noo3-w"] <- "no:ʕ"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "noo3-v"] <- "no:ʕ-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "beet-w"] <- "be:t"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "daas-w"] <- "da:s"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "diin-w"] <- "di:n"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "doom-w"] <- "do:m"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "doom-v"] <- "do:m-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "beet-w"] <- "be:t"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "maat-w"] <- "ma:t"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "maat-v"] <- "ma:t-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "mooz-w"] <- "mo:z"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "muun-w"] <- "mu:n"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "muus-w"] <- "mu:s"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "naam-w"] <- "na:m"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "naam-v"] <- "na:m-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "noom-w"] <- "no:m"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "saam-w"] <- "sa:m"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "saam-v"] <- "sa:m-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "beet-w"] <- "be:t"
ord.reRatingAgreeNas$context <- as.factor(ord.reRatingAgreeNas$context)
ord.reRatingAgreeNas$context <- as.vector(ord.reRatingAgreeNas$context) #get rid of factors
ord.reRatingAgreeNas$context = factor(ord.reRatingAgreeNas$context,ord.reRatingAgreeNas$context) #add ordered factors back
ord.reRatingAgreeNas3.1.7.2.2.2 Drawing the IRR figure
par(oma = c(0, 0, 0, 0))
plot(1:45, ord.reRatingAgreeNas$ICCVal, axes=FALSE, ylim=c(-1,1),
ylab="ICC. 95% CI", xlab="",cex=2,cex.lab=1.5,cex.main=1.5,
main="Agreement ICC for nasalisation",cex.axis=1.5)
axis(2,cex.lab=2,cex.axis=1.5)axis(1,at=1:45, labels = levels(ord.reRatingAgreeNas$context),
las=2,cex.lab=1.5,cex.axis=1.5)
for(i in 1:45) segments(i,ord.reRatingAgreeNas$lowerConfInt[i],
i,ord.reRatingAgreeNas$upperConfInt[i])abline(h = 0, lty=2)
abline(v = 20.5, lty=2)
Again, only the last two words showed a negative agreement between raters, with nearly half of the words receiving a statistically significant positive rating. And as expected, most of the words having nasal sounds sounds, received the highest ratings, with some having both nasals and pharyngeals receiving positive ratings.
3.1.8 Conclusion
Given the level of variance observed in the ICC scores for both consistency and agreements, it was necessary to use a statistical technique that takes into account this. It is possible that the raters were variables in some of their responses due to variable productions by the producing subjects and/or by the inherent differences between the raters.
Below we report on the results of the Cumulative Logit Mixed Model with subjects, raters and items as random effects.
3.2 Cumulative Logit Mixed Models (clmm)
The data were analysed using Cumulative Link Mixed Models (CLMM) using the “ordinal” package.
Given the structure of the data, we used a random effects structure to account for the design. A crossed random effects structure (for the producing subjects, the raters and the items) was used. A by-rater random slope for Context was used as it improved the model fit compared to a model with it.
Our outcome is the response entered as a factor (3 for Voice Quality; 5 for Nasalisation). Our predictor was Context with the reference level being set to “Isolation”.
We compared various combinations, e.g., by subject and item random slopes; interactions with vowel, etc. and all these did not improve the model fit. Hence the models reported below are the optimal ones
3.2.1 Models
3.2.1.1 Loading packages
Start by loading packages and install those that are not installed
requiredPackages = c('ordinal')
for(p in requiredPackages){
if(!require(p,character.only = TRUE)) install.packages(p)
library(p,character.only = TRUE)
}3.2.1.2 Reading the data
We then read in the data and look at the structure to verify the class of each column.
percVQDF <- read.csv("percVQDF.csv")
percNasDF <- read.csv("percNasDF.csv")
str(percVQDF)'data.frame': 2430 obs. of 6 variables:
$ X : int 1 2 3 4 5 6 7 8 9 10 ...
$ Response: int 3 2 3 3 3 2 3 3 3 3 ...
$ Context : Factor w/ 14 levels "3--3","3-n","3-o",..: 7 6 11 6 8 6 1 6 12 11 ...
$ Subject : Factor w/ 9 levels "p01","p02","p03",..: 4 4 4 4 4 4 4 4 4 4 ...
$ Item : Factor w/ 45 levels "3aaf-w","3aam-w",..: 38 37 24 23 41 40 8 7 27 19 ...
$ Rater : Factor w/ 6 levels "R01","R02","R03",..: 2 2 2 2 2 2 2 2 2 2 ...str(percNasDF)'data.frame': 2430 obs. of 6 variables:
$ X : int 1 2 3 4 5 6 7 8 9 10 ...
$ Response: int 4 4 4 3 3 4 2 3 2 4 ...
$ Context : Factor w/ 14 levels "3--3","3-n","3-o",..: 7 6 11 6 8 6 1 6 12 11 ...
$ Subject : Factor w/ 9 levels "p01","p02","p03",..: 4 4 4 4 4 4 4 4 4 4 ...
$ Item : Factor w/ 45 levels "3aaf-w","3aam-w",..: 38 37 24 23 41 40 8 7 27 19 ...
$ Rater : Factor w/ 6 levels "R01","R02","R03",..: 2 2 2 2 2 2 2 2 2 2 ...3.2.1.3 Changing a few things
We are changing a few things in both data-frames
Changing type of “response” to factor to suite the Cumulative Logit Mixed Model
Checking the levels of Context and changing the reference category to “Isolation”
Creating a new variable “ContextIPA” that contains the IPA symbols to be plotted in the figures below
# factor level
percVQDF$Response <- as.factor(percVQDF$Response)
percNasDF$Response <- as.factor(percNasDF$Response)
# Changing the reference level
levels(percVQDF$Context) [1] "3--3" "3-n" "3-o" "7-n" "7-o" "isolation"
[7] "n-3" "n-7" "n-n" "n-o" "o-3" "o-7"
[13] "o-n" "o-o" percVQDF$Context <- relevel(percVQDF$Context, ref="isolation")
levels(percNasDF$Context) [1] "3--3" "3-n" "3-o" "7-n" "7-o" "isolation"
[7] "n-3" "n-7" "n-n" "n-o" "o-3" "o-7"
[13] "o-n" "o-o" percNasDF$Context <- relevel(percNasDF$Context, ref="isolation")
# Creating a new variable "ContextIPA"
## for Voice Quallity
percVQDF$ContextIPA <- percVQDF$Context
percVQDF$ContextIPA <- as.character(percVQDF$ContextIPA)
percVQDF$ContextIPA[percVQDF$ContextIPA == "7-o"] <- "\u0127-o"
percVQDF$ContextIPA[percVQDF$ContextIPA == "o-7"] <- "o-\u0127"
percVQDF$ContextIPA[percVQDF$ContextIPA == "7-n"] <- "\u0127-n"
percVQDF$ContextIPA[percVQDF$ContextIPA == "n-7"] <- "n-\u0127"
percVQDF$ContextIPA[percVQDF$ContextIPA == "3-o"] <- "ʕ-o"
percVQDF$ContextIPA[percVQDF$ContextIPA == "o-3"] <- "o-ʕ"
percVQDF$ContextIPA[percVQDF$ContextIPA == "3-n"] <- "ʕ-n"
percVQDF$ContextIPA[percVQDF$ContextIPA == "n-3"] <- "n-ʕ"
percVQDF$ContextIPA[percVQDF$ContextIPA == "3--3"] <- "ʕ-ʕ"
percVQDF$ContextIPA[percVQDF$ContextIPA == "isolation"] <- "Isolation"
percVQDF$ContextIPA <- as.factor(percVQDF$ContextIPA)
percVQDF$ContextIPA <- relevel(percVQDF$ContextIPA, ref="Isolation")
levels(percVQDF$ContextIPA) [1] "Isolation" "ʕ-ʕ" "ʕ-n" "ʕ-o" "ħ-n" "ħ-o"
[7] "n-ʕ" "n-ħ" "n-n" "n-o" "o-ʕ" "o-ħ"
[13] "o-n" "o-o" # reordering levels of predictor
percVQDF$Context <- factor(percVQDF$Context, levels = c("isolation","7-n","7-o","n-7","n-n","n-o","n-3","o-7","o-n","o-o","o-3","3-n","3-o","3--3"))
percVQDF$ContextIPA <- factor(percVQDF$ContextIPA, levels = c("Isolation","\u0127-n","\u0127-o","n-\u0127","n-n","n-o","n-ʕ","o-\u0127","o-n","o-o","o-ʕ","ʕ-n","ʕ-o","ʕ-ʕ"))
levels(percVQDF$Context) [1] "isolation" "7-n" "7-o" "n-7" "n-n" "n-o"
[7] "n-3" "o-7" "o-n" "o-o" "o-3" "3-n"
[13] "3-o" "3--3" levels(percVQDF$ContextIPA) [1] "Isolation" "ħ-n" "ħ-o" "n-ħ" "n-n" "n-o"
[7] "n-ʕ" "o-ħ" "o-n" "o-o" "o-ʕ" "ʕ-n"
[13] "ʕ-o" "ʕ-ʕ" ## for Nasalisation
percNasDF$ContextIPA <- percNasDF$Context
percNasDF$ContextIPA <- as.character(percNasDF$ContextIPA)
percNasDF$ContextIPA[percNasDF$ContextIPA == "7-o"] <- "\u0127-o"
percNasDF$ContextIPA[percNasDF$ContextIPA == "o-7"] <- "o-\u0127"
percNasDF$ContextIPA[percNasDF$ContextIPA == "7-n"] <- "\u0127-n"
percNasDF$ContextIPA[percNasDF$ContextIPA == "n-7"] <- "n-\u0127"
percNasDF$ContextIPA[percNasDF$ContextIPA == "3-o"] <- "ʕ-o"
percNasDF$ContextIPA[percNasDF$ContextIPA == "o-3"] <- "o-ʕ"
percNasDF$ContextIPA[percNasDF$ContextIPA == "3-n"] <- "ʕ-n"
percNasDF$ContextIPA[percNasDF$ContextIPA == "n-3"] <- "n-ʕ"
percNasDF$ContextIPA[percNasDF$ContextIPA == "3--3"] <- "ʕ-ʕ"
percNasDF$ContextIPA[percNasDF$ContextIPA == "isolation"] <- "Isolation"
percNasDF$ContextIPA <- as.factor(percNasDF$ContextIPA)
percNasDF$ContextIPA <- relevel(percNasDF$ContextIPA, ref="Isolation")
levels(percNasDF$ContextIPA) [1] "Isolation" "ʕ-ʕ" "ʕ-n" "ʕ-o" "ħ-n" "ħ-o"
[7] "n-ʕ" "n-ħ" "n-n" "n-o" "o-ʕ" "o-ħ"
[13] "o-n" "o-o" # reordering levels of predictor
percNasDF$Context <- factor(percNasDF$Context, levels = c("isolation","7-n","7-o","n-7","n-n","n-o","n-3","o-7","o-n","o-o","o-3","3-n","3-o","3--3"))
percNasDF$ContextIPA <- factor(percNasDF$ContextIPA, levels = c("Isolation","\u0127-n","\u0127-o","n-\u0127","n-n","n-o","n-ʕ","o-\u0127","o-n","o-o","o-ʕ","ʕ-n","ʕ-o","ʕ-ʕ"))
levels(percNasDF$Context) [1] "isolation" "7-n" "7-o" "n-7" "n-n" "n-o"
[7] "n-3" "o-7" "o-n" "o-o" "o-3" "3-n"
[13] "3-o" "3--3" levels(percNasDF$ContextIPA) [1] "Isolation" "ħ-n" "ħ-o" "n-ħ" "n-n" "n-o"
[7] "n-ʕ" "o-ħ" "o-n" "o-o" "o-ʕ" "ʕ-n"
[13] "ʕ-o" "ʕ-ʕ" 3.2.2 Model specifications
As can be seem from the results of system time, it took roungly one hour to run the first model with a 4-Core machine running Microsoft R Open version 3.5.0 that ran the model using parallel computing (total around 4:30 hours). With the base R, it may take at least double this time to run, if not more given that with base R, only one core is used (unless specifically using parallel computing).
3.2.2.1 Voice Quality (VQ)
Below is our model specification for the full model that improved the model fit (we have tried various combinations, including by-Subject and by-Item random slopes for context; adding vowel as a fixed and random slope; consonant*vowel interactions, etc. However all these models either did not converge or did not improve the model fit compared to our model below)
3.2.2.1.1 Full model
system.time(fullCLMMVQSlope <- clmm(Response ~ Context + (1|Subject)+(1|Item)+(Context|Rater), data=percVQDF)) user system elapsed
12092.34 1770.03 3481.42 3.2.2.1.2 Null model
system.time(fullCLMMVQNull <- clmm(Response ~ 1 + (1|Subject)+(1|Item)+(Context|Rater), data=percVQDF)) user system elapsed
2465.16 0.18 2467.66 3.2.2.2 Nasalisation
Below is our model specification for the full model that improved the model fit (we have tried various combinations, including by-Subject and by-Item random slopes for context; adding vowel as a fixed and random slope; consonant*vowel interactions, etc. However all these models either did not converge or did not improve the model fit compared to our model below)
3.2.2.2.1 Full model
system.time(fullCLMMNasSlope <- clmm(Response ~ Context + (1|Subject)+(1|Item)+(Context|Rater), data=percNasDF)) user system elapsed
14250.61 2189.17 4125.80 3.2.2.2.2 Null model
system.time(fullCLMMNasNull <- clmm(Response ~ 1 + (1|Subject)+(1|Item)+(Context|Rater), data=percNasDF)) user system elapsed
4406.65 1.21 4422.56 3.2.3 Results
The results below are divided into those on Voice Quality (VQ) followed by those on Nasalisation.
3.2.3.1 Voice Quality (VQ)
3.2.3.1.1 Model comparisons
Through model comparison between the optimal model and the null model (aka Intercept only model), the results show that using our optimal model improved the model fit.
anova(fullCLMMVQNull,fullCLMMVQSlope)Likelihood ratio tests of cumulative link models:
no.par AIC logLik LR.stat df Pr(>Chisq)
fullCLMMVQNull 109 4019.0 -1900.5
fullCLMMVQSlope 122 4024.2 -1890.1 20.773 13 0.07754 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 13.2.3.1.2 Summary
Next the summary presented below shows the results for each of the coefficients (minus the Intercept) and of the thresholds (1|2 and 2|3).
Starting with the threshlods, the results show that overall, a decrease in ratings of tense voice quality is obtained for ratings between 1 and 2, which increases with ratings between 2 to 3. The coefficients of the 12 levels of the fixed effect “Context” show either negative or positive values. The negative ones indicate that the ratings of “tense” are decreased in these contexts (e.g., o-o or n-o) which obtained lower betas compared to the reference value “Isolation”. All positive values are associated with increase in ratings of “tense” voice quality, though some are not statistically significant.
summary(fullCLMMVQSlope)Cumulative Link Mixed Model fitted with the Laplace approximation
formula: Response ~ Context + (1 | Subject) + (1 | Item) + (Context | Rater)
data: percVQDF
Random effects:
Groups Name Variance Std.Dev. Corr
Item (Intercept) 0.15224 0.3902
Subject (Intercept) 0.21167 0.4601
Rater (Intercept) 0.87299 0.9343
Context7-n 0.72657 0.8524 -0.501
Context7-o 0.96219 0.9809 -0.471 0.960
Contextn-7 0.83035 0.9112 0.004 0.642 0.748
Contextn-n 0.08637 0.2939 -0.357 0.951 0.957 0.812
Contextn-o 0.68494 0.8276 -0.677 0.772 0.597 0.137 0.644
Contextn-3 0.56401 0.7510 0.549 0.302 0.342 0.750 0.435 -0.179
Contexto-7 0.22833 0.4778 -0.433 0.686 0.775 0.870 0.771 0.318
Contexto-n 0.14326 0.3785 0.219 -0.912 -0.811 -0.489 -0.842 -0.738
Contexto-o 0.12513 0.3537 -0.760 0.230 0.373 0.170 0.178 0.092
Contexto-3 0.78046 0.8834 -0.299 0.206 0.394 0.663 0.337 -0.169
Context3-n 0.75679 0.8699 -0.657 0.821 0.910 0.705 0.815 0.477
Context3-o 0.29034 0.5388 -0.528 0.765 0.906 0.687 0.789 0.337
Context3--3 2.58037 1.6064 -0.148 0.842 0.933 0.849 0.918 0.342
0.444
-0.387 -0.389
-0.344 0.558 0.154
0.273 0.833 0.160 0.724
0.188 0.894 -0.530 0.703 0.678
0.173 0.777 -0.510 0.654 0.610 0.945
0.550 0.709 -0.754 0.189 0.388 0.783 0.859
Number of groups: Item 45, Subject 9, Rater 6
Coefficients:
Estimate Std. Error z value Pr(>|z|)
Context7-n 0.991035 0.511102 1.939 0.052499 .
Context7-o 1.351805 0.511351 2.644 0.008203 **
Contextn-7 1.227045 0.533426 2.300 0.021431 *
Contextn-n 0.081502 0.334631 0.244 0.807573
Contextn-o -0.161309 0.460430 -0.350 0.726080
Contextn-3 0.969618 0.605949 1.600 0.109562
Contexto-7 1.088271 0.369180 2.948 0.003200 **
Contexto-n 0.218397 0.346296 0.631 0.528259
Contexto-o -0.005013 0.343046 -0.015 0.988340
Contexto-3 1.421408 0.479182 2.966 0.003014 **
Context3-n 1.679047 0.475315 3.532 0.000412 ***
Context3-o 2.536368 0.396778 6.392 1.63e-10 ***
Context3--3 3.001442 0.967364 3.103 0.001918 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Threshold coefficients:
Estimate Std. Error z value
1|2 -2.0512 0.4378 -4.685
2|3 1.6294 0.4365 3.7323.2.3.1.3 Figure
The summary of the results showed some statistically significant coefficients that displayed an association with increased ratings of “tense”. To allow us to visualise the ratings, we use the script below, which is a modified version of two scripts (see references in article).
The “beta” is the coefficient for each level of the fixed effect “Context” and “Theta” is the coefficient for each threshold of the ratings “Response”, 1|2, 2|3, etc…
## below changes the margins
par(oma=c(1, 0, 0, 3),mgp=c(2, 1, 0))
xlimVQ = c(min(fullCLMMVQSlope$beta), max(fullCLMMVQSlope$beta))
ylimVQ = c(0,1)
plot(0,0,xlim=xlimVQ, ylim=ylimVQ, type="n", ylab=expression(Probability), xlab="", xaxt = "n",main="Predicted curves - Voice Quality",cex=2,cex.lab=1.5,cex.main=1.5,cex.axis=1.5)
axis(side = 1, at = c(0,fullCLMMVQSlope$beta),labels = levels(percVQDF$ContextIPA), las=2,cex=2,cex.lab=1.5,cex.axis=1.5)xsVQ = seq(xlimVQ[1], xlimVQ[2], length.out=100)
lines(xsVQ, plogis(fullCLMMVQSlope$Theta[1] - xsVQ), col='black')
lines(xsVQ, plogis(fullCLMMVQSlope$Theta[2] - xsVQ)-plogis(fullCLMMVQSlope$Theta[1] - xsVQ), col='red')lines(xsVQ, 1- (plogis(fullCLMMVQSlope$Theta[2] - xsVQ)), col='blue')
abline(v=c(0,fullCLMMVQSlope$beta),lty=3)abline(h=0, lty="dashed")
abline(h=1, lty="dashed")legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1, col=c("black", "red", "blue"),
legend=c("Breathy", "Modal", "Tense"),cex=0.75)
The figure above show that ratings of “Breathy” received less than 15% specifically and this was mainly either in the “Isolation” context or contexts with nasal consonants. This same group showed increase in ratings of “Modal” voice at a rate close to 75%. Ratings associated with the “Tense” voice increased steadily from this first group until reaching the pharyngeal contexts, with the highest ratings of “Tense” being present in /ʕ-ʕ/ context at a rate of 80%, with the remaining 20% being associated with “Modal” voice
3.2.3.2 Nasalisation
3.2.3.2.1 Model comparisons
With Nasalisation, model comparisons showed again an improvement of the model fit with our optimal model.
anova(fullCLMMNasNull,fullCLMMNasSlope)Likelihood ratio tests of cumulative link models:
no.par AIC logLik LR.stat df Pr(>Chisq)
fullCLMMNasNull 111 6016.0 -2897.0
fullCLMMNasSlope 124 6014.9 -2883.5 27.103 13 0.01204 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 13.2.3.2.2 Summary
The summary of the model showed again a steady increase in ratings associated with nasal from 1 to 5. Ratings of 1|2 and 2|3 are not statistically significant; those from 3 to 5 are statistically and significantly associated with nasalisation. The coefficients of the 12 levels of the fixed effect are either negative, i.e., not associated with ratings of nasalisation, specifically when an oral context is in initial position, or when a pharyngeal is associated with an oral context. When pharyngeals are associated with nasals, ratings of nasalisation are increased.
summary(fullCLMMNasSlope)Cumulative Link Mixed Model fitted with the Laplace approximation
formula: Response ~ Context + (1 | Subject) + (1 | Item) + (Context | Rater)
data: percNasDF
Random effects:
Groups Name Variance Std.Dev. Corr
Item (Intercept) 0.08327 0.2886
Subject (Intercept) 0.08686 0.2947
Rater (Intercept) 2.06158 1.4358
Context7-n 1.23832 1.1128 -0.977
Context7-o 0.40683 0.6378 0.060 0.076
Contextn-7 1.09881 1.0482 -0.959 0.907 -0.141
Contextn-n 1.12499 1.0607 -0.947 0.944 0.015 0.966
Contextn-o 1.89869 1.3779 -0.988 0.972 0.033 0.952 0.963
Contextn-3 1.18456 1.0884 -0.936 0.921 0.146 0.951 0.972 0.970
Contexto-7 0.13583 0.3685 -0.104 0.090 0.669 0.206 0.227 0.205
Contexto-n 0.45232 0.6726 -0.856 0.857 0.270 0.841 0.902 0.926
Contexto-o 0.22277 0.4720 -0.823 0.855 0.463 0.812 0.874 0.872
Contexto-3 0.20164 0.4490 0.302 -0.322 0.564 -0.149 -0.153 -0.221
Context3-n 0.48303 0.6950 -0.697 0.782 0.632 0.588 0.716 0.773
Context3-o 0.91593 0.9570 0.517 -0.383 0.876 -0.544 -0.391 -0.429
Context3--3 2.11385 1.4539 0.663 -0.562 0.692 -0.777 -0.682 -0.601
0.406
0.958 0.462
0.938 0.583 0.915
0.004 0.894 0.033 0.202
0.793 0.480 0.877 0.900 0.071
-0.293 0.529 -0.161 0.029 0.640 0.207
-0.562 0.251 -0.386 -0.287 0.392 0.014 0.853
Number of groups: Item 45, Subject 9, Rater 6
Coefficients:
Estimate Std. Error z value Pr(>|z|)
Context7-n 2.9132 0.5458 5.337 9.43e-08 ***
Context7-o 0.2361 0.3665 0.644 0.5194
Contextn-7 3.0051 0.5272 5.700 1.20e-08 ***
Contextn-n 4.0126 0.5105 7.860 3.83e-15 ***
Contextn-o 4.0212 0.6239 6.446 1.15e-10 ***
Contextn-3 3.6125 0.6113 5.910 3.43e-09 ***
Contexto-7 -0.6175 0.3039 -2.032 0.0422 *
Contexto-n 3.1367 0.3768 8.324 < 2e-16 ***
Contexto-o -0.2339 0.3204 -0.730 0.4653
Contexto-3 -0.1837 0.3203 -0.573 0.5663
Context3-n 3.5573 0.3917 9.081 < 2e-16 ***
Context3-o -0.3729 0.4812 -0.775 0.4383
Context3--3 -1.6660 0.9537 -1.747 0.0807 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Threshold coefficients:
Estimate Std. Error z value
1|2 -0.6635 0.6068 -1.093
2|3 0.9297 0.6066 1.533
3|4 1.9334 0.6076 3.182
4|5 3.6733 0.6115 6.0073.2.3.2.3 Figure
## below changes the margins
par(oma=c(1, 0, 0, 3),mgp=c(2, 1, 0))
xlimNas = c(min(fullCLMMNasSlope$beta), max(fullCLMMNasSlope$beta))
ylimNas = c(0,1)
plot(0,0,xlim=xlimNas, ylim=ylimNas, type="n", ylab=expression(Probability), xlab="", xaxt = "n",main="Predicted curves - Nasalisation",cex=2,cex.lab=1.5,cex.main=1.5,cex.axis=1.5)
axis(side = 1, at = c(0,fullCLMMNasSlope$beta),labels = levels(percNasDF$ContextIPA), las=2,cex=2,cex.lab=1.5,cex.axis=1.5)xsNas = seq(xlimNas[1], xlimNas[2], length.out=100)
lines(xsNas, plogis(fullCLMMNasSlope$Theta[1] - xsNas), col='black')
lines(xsNas, plogis(fullCLMMNasSlope$Theta[2] - xsNas)-plogis(fullCLMMNasSlope$Theta[1] - xsNas), col='red')lines(xsNas, plogis(fullCLMMNasSlope$Theta[3] - xsNas)-plogis(fullCLMMNasSlope$Theta[2] - xsNas), col='green')
lines(xsNas, plogis(fullCLMMNasSlope$Theta[4] - xsNas)-plogis(fullCLMMNasSlope$Theta[3] - xsNas), col='orange')lines(xsNas, 1-(plogis(fullCLMMNasSlope$Theta[4] - xsNas)), col='blue')
abline(v=c(0,fullCLMMNasSlope$beta),lty=3)abline(h=0, lty="dashed")
abline(h=1, lty="dashed")legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1, col=c("black", "red", "green", "orange", "blue"),
legend=c("Oral", "2", "3", "4", "Nasal"),cex=0.75)
The figure above shows how two groups are formed. The first group includes all oral contexts, Isolation and pharyngeals either on their own or with an oral context. The second group include all nasal contexts and those including pharyngeals and nasals. It is interesting to see that the percept of nasality is increased when a pharyngeal is associated with a nasal context; when associated with an oral context, the ratings are mostly “Oral”. The context “n-o” and “n-n” received the highest ratings of “Nasal” (4 and 5) with a sum of around 85-90%. This was followed by both “n-ʕ” and “ʕ-n” at a combined rating of around 80%.
3.2.4 Exploring random effects
This section is not part of the analsyes presented in the article, but are crucial at understanding how the rating experiment worked and how the random factors were crucial in generalising the results. Without including random effects, the model would have provided wrong predictions, given that our subjects, raters and words showed clear variance. This was taken into account in the models presented above.
3.2.4.1 Voice Quality (VQ)
3.2.4.1.1 Preprocessing data
We start by preprocessing the data. We create data-frames with intercepts and variances of all random effects; the first column is the grouping factor, followed by 5 columns of intercepts, columns 7-11 are the variances.
####
#
#getting betas
betasVQ <- as.data.frame(fullCLMMVQSlope$beta)
# getting random effects coeficients and conditional variance
# for subjects
randomsVQSubject <- as.data.frame(ranef(fullCLMMVQSlope, condVar = F)$Subject)
varVQSubject <- as.data.frame(condVar(fullCLMMVQSlope)$Subject)
dfVQSubject <- merge(randomsVQSubject, varVQSubject, by ="row.names")
betasVQdfVQSubjectsubRanefVQSubj <- fullCLMMVQSlope$ranef[46:54]
subcondVarVQSubj <- condVar(fullCLMMVQSlope)$Subject[1]
subcondVarVQSubj <- matrix(as.numeric(unlist(subcondVarVQSubj)),nrow=nrow(subcondVarVQSubj))
# getting random effects coeficients and conditional variance
# for raters
randomsVQRater <- as.data.frame(ranef(fullCLMMVQSlope, condVar = F)$Rater)
varVQRater <- as.data.frame(condVar(fullCLMMVQSlope)$Rater)
dfVQRater <- merge(randomsVQRater, varVQRater, by ="row.names")
dfVQRatersubRanefVQRater <- fullCLMMVQSlope$ranef[55:60]
subcondVarVQRater <- condVar(fullCLMMVQSlope)$Rater[1]
subcondVarVQRater <- matrix(as.numeric(unlist(subcondVarVQRater)),nrow=nrow(subcondVarVQRater))
# getting random effects coeficients and conditional variance
# for words
randomsVQWord <- as.data.frame(ranef(fullCLMMVQSlope, condVar = F)$Item)
varVQWord <- as.data.frame(condVar(fullCLMMVQSlope)$Item)
dfVQWord <- merge(randomsVQWord, varVQWord, by ="row.names")
dfVQWordsubRanefVQWord <- fullCLMMVQSlope$ranef[1:45]
subcondVarVQWord <- condVar(fullCLMMVQSlope)$Item[1]
subcondVarVQWord <- matrix(as.numeric(unlist(subcondVarVQWord)),nrow=nrow(subcondVarVQWord)) We then reorder the random effects by their labels. We also change a few things
labelsVQ <- c(levels(percVQDF$Item),levels(percVQDF$Subject),
levels(percVQDF$Rater))
ord.reLabelVQ <- as.data.frame(fullCLMMVQSlope$ranef[1:60])
ord.reLabelVQ$var <- row.names(fullCLMMVQSlope$ranef[1:60])
ord.reLabelVQ <- merge(labelsVQ,ord.reLabelVQ,by ="row.names")
ord.reLabelVQord.reLabelVQ$Row.names <- as.numeric(ord.reLabelVQ$Row.names)
ord.reLabelVQ <- ord.reLabelVQ[ order(ord.reLabelVQ[,1]), ]
rownames(ord.reLabelVQ) <- NULL
ord.reLabelVQord.reLabelVQWord <- ord.reLabelVQ[1:45,]
ord.reLabelVQSubj <- ord.reLabelVQ[46:54,]
ord.reLabelVQRater <- ord.reLabelVQ[55:60,]
ord.reLabelVQWord$x <- factor(ord.reLabelVQWord$x)
ord.reLabelVQSubj$x <- factor(ord.reLabelVQSubj$x)
ord.reLabelVQRater$x <- factor(ord.reLabelVQRater$x)
#changing items from transliteration to IPA
ord.reLabelVQWord$x <- as.character(ord.reLabelVQWord$x)
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "7aad-w"] <- "\u0127a:d"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "7eef-w"] <- "\u0127e:f"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "7eef-v"] <- "\u0127e:f-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "7en-w"] <- "\u0127enn"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "7ook-w"] <- "\u0127o:k"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "7oom-w"] <- "\u0127o:m"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "loo7-w"] <- "lo:\u0127"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "naa7-w"] <- "na:\u0127"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "naa7-v"] <- "na:\u0127-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "nuu7-w"] <- "nu:\u0127"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "nuu7-v"] <- "nu:\u0127-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "zaa7-w"] <- "za:\u0127"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "zii7-w"] <- "zi:\u0127"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "3aaf-w"] <- "ʕa:f"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "3aam-w"] <- "ʕa:m"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "3eeb-w"] <- "ʕe:b"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "3eeb-v"] <- "ʕe:b-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "3een-w"] <- "ʕe:n"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "3iish-w"] <- "ʕa:ʃ"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "3oo3-w"] <- "ʕo:ʕ"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "3oo3-v"] <- "ʕo:ʕ-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "3oon-w"] <- "ʕo:n"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "baa3-w"] <- "ba:ʕ"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "bii3-w"] <- "bi:ʕ"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "bii3-v"] <- "bi:ʕ-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "boosh-w"] <- "bo:ʃ"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "djuu3-w"] <- "d͡ʒu:ʕ"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "djuu3-v"] <- "d͡ʒu:ʕ-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "noo3-w"] <- "no:ʕ"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "noo3-v"] <- "no:ʕ-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "beet-w"] <- "be:t"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "daas-w"] <- "da:s"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "diin-w"] <- "di:n"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "doom-w"] <- "do:m"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "doom-v"] <- "do:m-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "beet-w"] <- "be:t"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "maat-w"] <- "ma:t"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "maat-v"] <- "ma:t-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "mooz-w"] <- "mo:z"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "muun-w"] <- "mu:n"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "muus-w"] <- "mu:s"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "naam-w"] <- "na:m"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "naam-v"] <- "na:m-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "noom-w"] <- "no:m"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "saam-w"] <- "sa:m"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "saam-v"] <- "sa:m-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "beet-w"] <- "be:t"
ord.reLabelVQWord$x <- as.factor(ord.reLabelVQWord$x)
# reordering
ord.reLabelVQWord <- ord.reLabelVQWord %>% arrange(`fullCLMMVQSlope$ranef[1:60]`)
ord.reLabelVQWord$x <- as.vector(ord.reLabelVQWord$x) #get rid of factors
ord.reLabelVQWord$x = factor(ord.reLabelVQWord$x,ord.reLabelVQWord$x) #add ordered factors back
ord.reLabelVQSubj <- ord.reLabelVQSubj %>% arrange(`fullCLMMVQSlope$ranef[1:60]`)
ord.reLabelVQSubj$x <- as.vector(ord.reLabelVQSubj$x) #get rid of factors
ord.reLabelVQSubj$x = factor(ord.reLabelVQSubj$x,ord.reLabelVQSubj$x) #add ordered factors back
ord.reLabelVQRater <- ord.reLabelVQRater %>% arrange(`fullCLMMVQSlope$ranef[1:60]`)
ord.reLabelVQRater$x <- as.vector(ord.reLabelVQRater$x) #get rid of factors
ord.reLabelVQRater$x = factor(ord.reLabelVQRater$x,ord.reLabelVQRater$x) #add ordered factors back3.2.4.1.2 Figures
3.2.4.1.2.1 Subjects
ciVQSubj <- subRanefVQSubj + qnorm(0.975) * sqrt(as.numeric(subcondVarVQSubj)) %o% c(-1, 1)
ord.reVQSubj <- order(subRanefVQSubj)
ciVQSubj <- ciVQSubj[order(subRanefVQSubj),]
plot(subRanefVQSubj[ord.reVQSubj], 1:9, axes=FALSE, xlim=range(ciVQSubj),
xlab="Subject variation", ylab="",cex=2,cex.lab=2,cex.main=2,
main="BLUPs Subject VQ",cex.axis=1.5)
axis(1,cex.lab=2,cex.axis=1.5)axis(2,at=1:9, labels = levels(ord.reLabelVQSubj$x), las=2,cex.lab=2,cex.axis=1.5)
for(i in 1:9) segments(ciVQSubj[i,1], i, ciVQSubj[i, 2],i)abline(v = 0, lty=2)
It is interesting to see that not all subjects (who produced the items) were rated as more or less “tense”. The first two subjects (2 and 3) contributed more to the percept of “tense” voice quality whereas speakers 6, 9 and 4, contributed less to this percept.
3.2.4.1.2.2 Raters
ciVQRater <- subRanefVQRater + qnorm(0.975) * sqrt(as.numeric(subcondVarVQRater)) %o% c(-1, 1)
ord.reVQRater <- order(subRanefVQRater)
ciVQRater <- ciVQRater[order(subRanefVQRater),]
plot(subRanefVQRater[ord.reVQRater], 1:6, axes=FALSE, xlim=range(ciVQRater),
xlab="Rater variation", ylab="",cex=2,cex.lab=2,cex.main=2,
main="BLUPs Rater VQ",cex.axis=1.5)
axis(1,cex.lab=2,cex.axis=1.5)axis(2,at=1:6, labels = levels(ord.reLabelVQRater$x), las=2,cex.lab=2,cex.axis=1.5)
for(i in 1:6) segments(ciVQRater[i,1], i, ciVQRater[i, 2],i)abline(v = 0, lty=2)
With respect to raters, it is interesting to see that there is a clear split between raters (that was already picked up in the IRR analyses above). The first three (2, 5 and 6) were more likely to provide postive responses to “tense” whereas 1 and 4 (and potentially 3) were more conservative in their responses. R01 is the first author and R02 is the second. This shows that R01 is more conservative in their responses than R02.
3.2.4.1.2.3 Items
ciVQWord <- subRanefVQWord + qnorm(0.975) * sqrt(as.numeric(subcondVarVQWord)) %o% c(-1, 1)
ord.reVQWord <- order(subRanefVQWord)
ciVQWord <- ciVQWord[order(subRanefVQWord),]
par(oma = c(0, 1.5, 0, 0))
plot(1:45,subRanefVQWord[ord.reVQWord], axes=FALSE, ylim=range(ciVQWord),
ylab="Item variation", xlab="",cex=2,cex.lab=2,cex.main=2,
main="BLUPs Item VQ",cex.axis=1.5)
axis(2,cex.lab=2,cex.axis=1.5)axis(1,at=1:45, labels = levels(ord.reLabelVQWord$x), las=2,cex.lab=2,cex.axis=1.5)
for(i in 1:45) segments(i, ciVQWord[i,1], i, ciVQWord[i, 2])abline(h = 0, lty=2)
With item variation, not all words contributed positively to the “tense” quality and there is a clear gradation in impact of words on ratings. This is important to take into account of course in our model
3.2.4.2 Nasalisation
3.2.4.2.1 Preprocessing data
We start by preprocessing the data. We create data-frames with intercepts and variances of all random effects; the first column is the grouping factor, followed by 5 columns of intercepts, columns 7-11 are the variances.
####
#
#getting betas
betasNas <- as.data.frame(fullCLMMNasSlope$beta)
# getting random effects coeficients and conditional variance
# for subjects
randomsNasSubject <- as.data.frame(ranef(fullCLMMNasSlope, condVar = F)$Subject)
varNasSubject <- as.data.frame(condVar(fullCLMMNasSlope)$Subject)
dfNasSubject <- merge(randomsNasSubject, varNasSubject, by ="row.names")
betasNasdfNasSubjectsubRanefNasSubj <- fullCLMMNasSlope$ranef[46:54]
subcondVarNasSubj <- condVar(fullCLMMNasSlope)$Subject[1]
subcondVarNasSubj <- matrix(as.numeric(unlist(subcondVarNasSubj)),nrow=nrow(subcondVarNasSubj))
# getting random effects coeficients and conditional variance
# for raters
randomsNasRater <- as.data.frame(ranef(fullCLMMNasSlope, condVar = F)$Rater)
varNasRater <- as.data.frame(condVar(fullCLMMNasSlope)$Rater)
dfNasRater <- merge(randomsNasRater, varNasRater, by ="row.names")
dfNasRatersubRanefNasRater <- fullCLMMNasSlope$ranef[55:60]
subcondVarNasRater <- condVar(fullCLMMNasSlope)$Rater[1]
subcondVarNasRater <- matrix(as.numeric(unlist(subcondVarNasRater)),nrow=nrow(subcondVarNasRater))
# getting random effects coeficients and conditional variance
# for words
randomsNasWord <- as.data.frame(ranef(fullCLMMNasSlope, condVar = F)$Item)
varNasWord <- as.data.frame(condVar(fullCLMMNasSlope)$Item)
dfNasWord <- merge(randomsNasWord, varNasWord, by ="row.names")
dfNasWordsubRanefNasWord <- fullCLMMNasSlope$ranef[1:45]
subcondVarNasWord <- condVar(fullCLMMNasSlope)$Item[1]
subcondVarNasWord <- matrix(as.numeric(unlist(subcondVarNasWord)),nrow=nrow(subcondVarNasWord)) We then reorder the random effects by their labels. We also change a few things
labelsNas <- c(levels(percNasDF$Item),levels(percNasDF$Subject),levels(percNasDF$Rater))
ord.reLabelNas <- as.data.frame(fullCLMMNasSlope$ranef[1:60])
ord.reLabelNas$var <- row.names(fullCLMMNasSlope$ranef[1:60])
ord.reLabelNas <- merge(labelsNas,ord.reLabelNas,by ="row.names")
ord.reLabelNasord.reLabelNas$Row.names <- as.numeric(ord.reLabelNas$Row.names)
ord.reLabelNas <- ord.reLabelNas[ order(ord.reLabelNas[,1]), ]
rownames(ord.reLabelNas) <- NULL
ord.reLabelNasord.reLabelNasWord <- ord.reLabelNas[1:45,]
ord.reLabelNasSubj <- ord.reLabelNas[46:54,]
ord.reLabelNasRater <- ord.reLabelNas[55:60,]
ord.reLabelNasWord$x <- factor(ord.reLabelNasWord$x)
ord.reLabelNasSubj$x <- factor(ord.reLabelNasSubj$x)
ord.reLabelNasRater$x <- factor(ord.reLabelNasRater$x)
#changing items from transliteration to IPA
ord.reLabelNasWord$x <- as.character(ord.reLabelNasWord$x)
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "7aad-w"] <- "\u0127a:d"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "7eef-w"] <- "\u0127e:f"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "7eef-v"] <- "\u0127e:f-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "7en-w"] <- "\u0127enn"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "7ook-w"] <- "\u0127o:k"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "7oom-w"] <- "\u0127o:m"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "loo7-w"] <- "lo:\u0127"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "naa7-w"] <- "na:\u0127"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "naa7-v"] <- "na:\u0127-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "nuu7-w"] <- "nu:\u0127"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "nuu7-v"] <- "nu:\u0127-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "zaa7-w"] <- "za:\u0127"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "zii7-w"] <- "zi:\u0127"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "3aaf-w"] <- "ʕa:f"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "3aam-w"] <- "ʕa:m"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "3eeb-w"] <- "ʕe:b"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "3eeb-v"] <- "ʕe:b-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "3een-w"] <- "ʕe:n"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "3iish-w"] <- "ʕa:ʃ"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "3oo3-w"] <- "ʕo:ʕ"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "3oo3-v"] <- "ʕo:ʕ-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "3oon-w"] <- "ʕo:n"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "baa3-w"] <- "ba:ʕ"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "bii3-w"] <- "bi:ʕ"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "bii3-v"] <- "bi:ʕ-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "boosh-w"] <- "bo:ʃ"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "djuu3-w"] <- "d͡ʒu:ʕ"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "djuu3-v"] <- "d͡ʒu:ʕ-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "noo3-w"] <- "no:ʕ"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "noo3-v"] <- "no:ʕ-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "beet-w"] <- "be:t"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "daas-w"] <- "da:s"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "diin-w"] <- "di:n"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "doom-w"] <- "do:m"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "doom-v"] <- "do:m-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "beet-w"] <- "be:t"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "maat-w"] <- "ma:t"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "maat-v"] <- "ma:t-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "mooz-w"] <- "mo:z"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "muun-w"] <- "mu:n"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "muus-w"] <- "mu:s"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "naam-w"] <- "na:m"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "naam-v"] <- "na:m-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "noom-w"] <- "no:m"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "saam-w"] <- "sa:m"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "saam-v"] <- "sa:m-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "beet-w"] <- "be:t"
ord.reLabelNasWord$x <- as.factor(ord.reLabelNasWord$x)
# reordering
ord.reLabelNasWord <- ord.reLabelNasWord %>% arrange(`fullCLMMNasSlope$ranef[1:60]`)
ord.reLabelNasWord$x <- as.vector(ord.reLabelNasWord$x) #get rid of factors
ord.reLabelNasWord$x = factor(ord.reLabelNasWord$x,ord.reLabelNasWord$x) #add ordered factors back
ord.reLabelNasSubj <- ord.reLabelNasSubj %>% arrange(`fullCLMMNasSlope$ranef[1:60]`)
ord.reLabelNasSubj$x <- as.vector(ord.reLabelNasSubj$x) #get rid of factors
ord.reLabelNasSubj$x = factor(ord.reLabelNasSubj$x,ord.reLabelNasSubj$x) #add ordered factors back
ord.reLabelNasRater <- ord.reLabelNasRater %>% arrange(`fullCLMMNasSlope$ranef[1:60]`)
ord.reLabelNasRater$x <- as.vector(ord.reLabelNasRater$x) #get rid of factors
ord.reLabelNasRater$x = factor(ord.reLabelNasRater$x,ord.reLabelNasRater$x) #add ordered factors back3.2.4.2.2 Figures
3.2.4.2.2.1 Subjects
ciNasSubj <- subRanefNasSubj + qnorm(0.975) * sqrt(as.numeric(subcondVarNasSubj)) %o% c(-1, 1)
ord.reNasSubj <- order(subRanefNasSubj)
ciNasSubj <- ciNasSubj[order(subRanefNasSubj),]
plot(subRanefNasSubj[ord.reNasSubj], 1:9, axes=FALSE, xlim=range(ciNasSubj),
xlab="Subject variation", ylab="",cex=2,cex.lab=2,cex.main=2,
main="BLUPs Subject Nas",cex.axis=1.5)
axis(1,cex.lab=2,cex.axis=1.5)axis(2,at=1:9, labels = levels(ord.reLabelNasSubj$x), las=2,cex.lab=2,cex.axis=1.5)
for(i in 1:9) segments(ciNasSubj[i,1], i, ciNasSubj[i, 2],i)abline(v = 0, lty=2)
As with the ratings of VQ, some subjects were producing sounds that were judged as more or less nasal. It is interesting to note that subject p02 was judged as being the one contributing the most to the percept of nasalisation and to the percept of tense voice quality seen above; the same pattern applies to subject p06 who is contributing the least to the percept of nasalisation and tense voice quality.
3.2.4.2.2.2 Raters
ciNasRater <- subRanefNasRater + qnorm(0.975) * sqrt(as.numeric(subcondVarNasRater)) %o% c(-1, 1)
ord.reNasRater <- order(subRanefNasRater)
ciNasRater <- ciNasRater[order(subRanefNasRater),]
plot(subRanefNasRater[ord.reNasRater], 1:6, axes=FALSE, xlim=range(ciNasRater),
xlab="Rater variation", ylab="",cex=2,cex.lab=2,cex.main=2,
main="BLUPs Rater Nas",cex.axis=1.5)
axis(1,cex.lab=2,cex.axis=1.5)axis(2,at=1:6, labels = levels(ord.reLabelNasRater$x), las=2,cex.lab=2,cex.axis=1.5)
for(i in 1:6) segments(ciNasRater[i,1], i, ciNasRater[i, 2],i)abline(v = 0, lty=2)
With respect to random effects related to the rater, there is a clear clustering with four rater being more favorable to rating “nasality” more often that the remaining two. Raters R01 and R02 (first and second authors, respectively) are equally postively providing ratings. associated with the percept of nasality.
3.2.4.2.2.3 Items
ciNasWord <- subRanefNasWord + qnorm(0.975) * sqrt(as.numeric(subcondVarNasWord)) %o% c(-1, 1)
ord.reNasWord <- order(subRanefNasWord)
ciNasWord <- ciNasWord[order(subRanefNasWord),]
par(oma = c(0, 1.5, 0, 0))
plot(1:45,subRanefNasWord[ord.reNasWord], axes=FALSE, ylim=range(ciNasWord),
ylab="Item variation", xlab="",cex=2,cex.lab=2,cex.main=2,
main="BLUPs Item Nas",cex.axis=1.5)
axis(2,cex.lab=2,cex.axis=1.5)axis(1,at=1:45, labels = levels(ord.reLabelNasWord$x), las=2,cex.lab=2,cex.axis=1.5)
for(i in 1:45) segments(i, ciNasWord[i,1], i, ciNasWord[i, 2])abline(h = 0, lty=2)
As above, various items were rated as more nasal than others. What is interesting to note is that all Items that were rated as more nasal contained a [+back] and +[high] vowels, and the only word that was significantly rated as not contributing to nasality is the one containing /ʕ-ʕ/.
---
title: "Rating VQ and Nasalisation Pharyngeals"
author: 
  name: "Jalal Al-Tamimi"
  affiliation: "Newcastle University"
date: "09 June 2018"
output: 
  html_notebook:
    number_sections: true
    toc: true
    toc_depth: 6
    toc_float:
      collapsed: true
---

# Introduction
This analysis accompanies the article "Khattab, G., Al-Tamimi, J., and Al-Siraih, W., (2018). Nasalisation in the production of Iraqi Arabic pharyngeals. Phonetica. DOI: https://10.1159/000487806". 
Below is an analysis of the perceptual experiment that was run to evaluate raters impressions of voice quality changes and/or nasalisation that was present within pharyngeals in Iraqi Arabic. 

# Material
Data were recorded from nine Iraqi Arabic speakers producing a list of items containing the two pharyngeal consonants /ħ ʕ/ and either an oral context (i.e., oral consonant), a nasal context (i.e., nasal consonant), or only two pharyngeals (/ʕ-ʕ/). The "control" contexts were oral-oral, oral-nasal, nasal-oral, nasal-nasal, and Isolation (which was all vowels in the items produced in isolation). The items were in a CVC environment. 

Our aim in this analysis was to evaluate the impression of harsh/tense voice quality and/or nasalisation in the pharyngeal contexts, as compared with all other contexts specified above was perceptually salient or not. For this, six phonetically trained raters (including the first two authors) rated these items using Praat's MFC experiment. The data used here were a subset of the data (around 20% of the full dataset). The first author rated all items from the full dataset and the results obtained from the full set and the subset are comparable. We ran this as a rating experiment, with three levels rating for Voice Quality (experiment 1) and five levels rating for Nasalisation (experiment 2). For more details, see the article.

# Statistical analyses
The results of the rating experiment were analysed using an Inter-Rater Reliability test and then using a Cumulative Logit Mixed Models. The aim of these two analsyes is to shed light into how pharyngeals, when in a nasal context show clear patterns of a nasal context.

## Inter-Rater Reliability
We started by evaluating Inter-Rater Reliability (IRR) using Intra-Class Correlations (ICC) on each of the Voice Quality and the Nasalisation rating experiments. We were interested in both consistency and agreement between the six rater. Results in the article are reported only for the agreement. Here we present and discuss both.

### Loading required packages

```{r warning=FALSE, message=FALSE, error=FALSE}
requiredPackages = c('dplyr','irr','reshape2')
for(p in requiredPackages){
  if(!require(p,character.only = TRUE)) install.packages(p)
  library(p,character.only = TRUE)
}
```

### Reading in data

We read in the data and check the structure

```{r warning=FALSE, message=FALSE, error=FALSE}
pharyngealsVQIRR <- read.csv("pharyngealsVQIRR.csv")
pharyngealsNasIRR <- read.csv("pharyngealsNasIRR.csv")

str(pharyngealsVQIRR)
str(pharyngealsNasIRR)
```

### Melting and casting the data

We change a few points in the data-frame. We transform the responses into numbers. We then melt and decast the data to have it in a wide format. The new structure has 9 rows (representing data from each producing subject) and 270 columns with the ratings from each word and rater ID. 
```{r warning=FALSE, message=FALSE, error=FALSE}
## change response into a number
pharyngealsVQIRR$responseNumb <- as.numeric(pharyngealsVQIRR$responseNumb)
pharyngealsNasIRR$responseNumb <- as.numeric(pharyngealsNasIRR$responseNumb)

## casting and melting the data frame, VQ first
pharyngealsVQIRRCast <- dcast(melt(pharyngealsVQIRR, id.vars=c("subjectNew", "wordTarget","raterNumber")), subjectNew~wordTarget+raterNumber)

## casting and melting the data frame, nasalisation second
pharyngealsNasIRRCast <- dcast(melt(pharyngealsNasIRR, id.vars=c("subjectNew", "wordTarget","raterNumber")), subjectNew~wordTarget+raterNumber)

str(pharyngealsVQIRRCast)
```

### Intra-Class Correlations (ICC)
We used ICCs to compute inter-rater reliability. For details see the article. We ran two types of tests. We wanted to check how consistent the raters were in their choices and also how they agree between each other. The agreements were reported in the article. We start by reporting the overall consistency and agreement levels for each of the VQ and Nasalisation experiments and then on the consistency and agreement on each word, i.e., an Item-based analysis.

#### Voice Quality (VQ)
We start with the VQ experiment.

##### Consistency
Below are the model specifications for the consistency.

```{r}
VQICCRatingConsWordAver <- icc(pharyngealsVQIRRCast[2:271],model="twoway",type="consistency",unit="average")

VQICCRatingConsWordAver3aafw <- icc(pharyngealsVQIRRCast[2:7],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver3aamw <- icc(pharyngealsVQIRRCast[8:13],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver3eebv <- icc(pharyngealsVQIRRCast[14:19],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver3eebw <- icc(pharyngealsVQIRRCast[20:25],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver3eenw <- icc(pharyngealsVQIRRCast[26:31],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver3iishw <- icc(pharyngealsVQIRRCast[32:37],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver3oo3v <- icc(pharyngealsVQIRRCast[38:43],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver3oo3w <- icc(pharyngealsVQIRRCast[44:49],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver3oonw <- icc(pharyngealsVQIRRCast[50:55],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver7aadw <- icc(pharyngealsVQIRRCast[56:61],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver7eefv <- icc(pharyngealsVQIRRCast[62:67],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver7eefw <- icc(pharyngealsVQIRRCast[68:73],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver7enw <- icc(pharyngealsVQIRRCast[74:79],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver7ookw <- icc(pharyngealsVQIRRCast[80:85],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAver7oomw <- icc(pharyngealsVQIRRCast[86:91],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverbaa3w <- icc(pharyngealsVQIRRCast[92:97],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverbeetw <- icc(pharyngealsVQIRRCast[98:103],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverbii3v <- icc(pharyngealsVQIRRCast[104:109],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverbii3w <- icc(pharyngealsVQIRRCast[110:115],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverbooshw <- icc(pharyngealsVQIRRCast[116:121],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverdaasw <- icc(pharyngealsVQIRRCast[122:127],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverdiinw <- icc(pharyngealsVQIRRCast[128:133],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverdjuu3v <- icc(pharyngealsVQIRRCast[134:139],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverdjuu3w <- icc(pharyngealsVQIRRCast[140:145],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverdoomv <- icc(pharyngealsVQIRRCast[146:151],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverdoomw <- icc(pharyngealsVQIRRCast[152:157],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverloo7w <- icc(pharyngealsVQIRRCast[158:163],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvermaatv <- icc(pharyngealsVQIRRCast[164:169],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvermaatw <- icc(pharyngealsVQIRRCast[170:175],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvermoozw <- icc(pharyngealsVQIRRCast[176:181],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvermuunw <- icc(pharyngealsVQIRRCast[182:187],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvermuusw <- icc(pharyngealsVQIRRCast[188:193],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvernaa7v <- icc(pharyngealsVQIRRCast[194:199],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvernaa7w <- icc(pharyngealsVQIRRCast[200:205],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvernaamv <- icc(pharyngealsVQIRRCast[206:211],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvernaamw <- icc(pharyngealsVQIRRCast[212:217],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvernoo3v <- icc(pharyngealsVQIRRCast[218:223],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvernoo3w <- icc(pharyngealsVQIRRCast[224:229],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvernoomw <- icc(pharyngealsVQIRRCast[230:235],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvernuu7v <- icc(pharyngealsVQIRRCast[236:241],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAvernuu7w <- icc(pharyngealsVQIRRCast[242:247],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAversaamv <- icc(pharyngealsVQIRRCast[248:253],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAversaamw <- icc(pharyngealsVQIRRCast[254:259],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverzaa7w <- icc(pharyngealsVQIRRCast[260:265],model="twoway",type="consistency",unit="average")
VQICCRatingConsWordAverzii7w <- icc(pharyngealsVQIRRCast[266:271],model="twoway",type="consistency",unit="average")

```

##### Agreement
Below are the model specifications for the agreement

```{r}

VQICCRatingAgreeWordAver <- icc(pharyngealsVQIRRCast[2:271],model="twoway",type="agreement",unit="average")

VQICCRatingAgreeWordAver3aafw <- icc(pharyngealsVQIRRCast[2:7],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver3aamw <- icc(pharyngealsVQIRRCast[8:13],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver3eebv <- icc(pharyngealsVQIRRCast[14:19],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver3eebw <- icc(pharyngealsVQIRRCast[20:25],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver3eenw <- icc(pharyngealsVQIRRCast[26:31],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver3iishw <- icc(pharyngealsVQIRRCast[32:37],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver3oo3v <- icc(pharyngealsVQIRRCast[38:43],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver3oo3w <- icc(pharyngealsVQIRRCast[44:49],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver3oonw <- icc(pharyngealsVQIRRCast[50:55],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver7aadw <- icc(pharyngealsVQIRRCast[56:61],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver7eefv <- icc(pharyngealsVQIRRCast[62:67],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver7eefw <- icc(pharyngealsVQIRRCast[68:73],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver7enw <- icc(pharyngealsVQIRRCast[74:79],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver7ookw <- icc(pharyngealsVQIRRCast[80:85],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAver7oomw <- icc(pharyngealsVQIRRCast[86:91],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverbaa3w <- icc(pharyngealsVQIRRCast[92:97],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverbeetw <- icc(pharyngealsVQIRRCast[98:103],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverbii3v <- icc(pharyngealsVQIRRCast[104:109],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverbii3w <- icc(pharyngealsVQIRRCast[110:115],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverbooshw <- icc(pharyngealsVQIRRCast[116:121],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverdaasw <- icc(pharyngealsVQIRRCast[122:127],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverdiinw <- icc(pharyngealsVQIRRCast[128:133],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverdjuu3v <- icc(pharyngealsVQIRRCast[134:139],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverdjuu3w <- icc(pharyngealsVQIRRCast[140:145],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverdoomv <- icc(pharyngealsVQIRRCast[146:151],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverdoomw <- icc(pharyngealsVQIRRCast[152:157],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverloo7w <- icc(pharyngealsVQIRRCast[158:163],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvermaatv <- icc(pharyngealsVQIRRCast[164:169],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvermaatw <- icc(pharyngealsVQIRRCast[170:175],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvermoozw <- icc(pharyngealsVQIRRCast[176:181],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvermuunw <- icc(pharyngealsVQIRRCast[182:187],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvermuusw <- icc(pharyngealsVQIRRCast[188:193],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvernaa7v <- icc(pharyngealsVQIRRCast[194:199],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvernaa7w <- icc(pharyngealsVQIRRCast[200:205],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvernaamv <- icc(pharyngealsVQIRRCast[206:211],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvernaamw <- icc(pharyngealsVQIRRCast[212:217],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvernoo3v <- icc(pharyngealsVQIRRCast[218:223],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvernoo3w <- icc(pharyngealsVQIRRCast[224:229],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvernoomw <- icc(pharyngealsVQIRRCast[230:235],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvernuu7v <- icc(pharyngealsVQIRRCast[236:241],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAvernuu7w <- icc(pharyngealsVQIRRCast[242:247],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAversaamv <- icc(pharyngealsVQIRRCast[248:253],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAversaamw <- icc(pharyngealsVQIRRCast[254:259],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverzaa7w <- icc(pharyngealsVQIRRCast[260:265],model="twoway",type="agreement",unit="average")
VQICCRatingAgreeWordAverzii7w <- icc(pharyngealsVQIRRCast[266:271],model="twoway",type="agreement",unit="average")

```

#### Nasalisation
We move to the nasalisation experiment.

##### Consistency
Below are the model specifications for the consistency.

```{r}
NasICCRatingConsWordAver <- icc(pharyngealsNasIRRCast[2:271],model="twoway",type="consistency",unit="average")

NasICCRatingConsWordAver3aafw <- icc(pharyngealsNasIRRCast[2:7],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver3aamw <- icc(pharyngealsNasIRRCast[8:13],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver3eebv <- icc(pharyngealsNasIRRCast[14:19],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver3eebw <- icc(pharyngealsNasIRRCast[20:25],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver3eenw <- icc(pharyngealsNasIRRCast[26:31],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver3iishw <- icc(pharyngealsNasIRRCast[32:37],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver3oo3v <- icc(pharyngealsNasIRRCast[38:43],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver3oo3w <- icc(pharyngealsNasIRRCast[44:49],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver3oonw <- icc(pharyngealsNasIRRCast[50:55],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver7aadw <- icc(pharyngealsNasIRRCast[56:61],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver7eefv <- icc(pharyngealsNasIRRCast[62:67],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver7eefw <- icc(pharyngealsNasIRRCast[68:73],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver7enw <- icc(pharyngealsNasIRRCast[74:79],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver7ookw <- icc(pharyngealsNasIRRCast[80:85],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAver7oomw <- icc(pharyngealsNasIRRCast[86:91],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverbaa3w <- icc(pharyngealsNasIRRCast[92:97],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverbeetw <- icc(pharyngealsNasIRRCast[98:103],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverbii3v <- icc(pharyngealsNasIRRCast[104:109],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverbii3w <- icc(pharyngealsNasIRRCast[110:115],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverbooshw <- icc(pharyngealsNasIRRCast[116:121],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverdaasw <- icc(pharyngealsNasIRRCast[122:127],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverdiinw <- icc(pharyngealsNasIRRCast[128:133],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverdjuu3v <- icc(pharyngealsNasIRRCast[134:139],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverdjuu3w <- icc(pharyngealsNasIRRCast[140:145],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverdoomv <- icc(pharyngealsNasIRRCast[146:151],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverdoomw <- icc(pharyngealsNasIRRCast[152:157],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverloo7w <- icc(pharyngealsNasIRRCast[158:163],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvermaatv <- icc(pharyngealsNasIRRCast[164:169],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvermaatw <- icc(pharyngealsNasIRRCast[170:175],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvermoozw <- icc(pharyngealsNasIRRCast[176:181],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvermuunw <- icc(pharyngealsNasIRRCast[182:187],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvermuusw <- icc(pharyngealsNasIRRCast[188:193],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvernaa7v <- icc(pharyngealsNasIRRCast[194:199],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvernaa7w <- icc(pharyngealsNasIRRCast[200:205],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvernaamv <- icc(pharyngealsNasIRRCast[206:211],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvernaamw <- icc(pharyngealsNasIRRCast[212:217],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvernoo3v <- icc(pharyngealsNasIRRCast[218:223],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvernoo3w <- icc(pharyngealsNasIRRCast[224:229],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvernoomw <- icc(pharyngealsNasIRRCast[230:235],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvernuu7v <- icc(pharyngealsNasIRRCast[236:241],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAvernuu7w <- icc(pharyngealsNasIRRCast[242:247],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAversaamv <- icc(pharyngealsNasIRRCast[248:253],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAversaamw <- icc(pharyngealsNasIRRCast[254:259],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverzaa7w <- icc(pharyngealsNasIRRCast[260:265],model="twoway",type="consistency",unit="average")
NasICCRatingConsWordAverzii7w <- icc(pharyngealsNasIRRCast[266:271],model="twoway",type="consistency",unit="average")

```

##### Agreement
Below are the model specifications for the agreement

```{r}

NasICCRatingAgreeWordAver <- icc(pharyngealsNasIRRCast[2:271],model="twoway",type="agreement",unit="average")

NasICCRatingAgreeWordAver3aafw <- icc(pharyngealsNasIRRCast[2:7],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver3aamw <- icc(pharyngealsNasIRRCast[8:13],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver3eebv <- icc(pharyngealsNasIRRCast[14:19],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver3eebw <- icc(pharyngealsNasIRRCast[20:25],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver3eenw <- icc(pharyngealsNasIRRCast[26:31],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver3iishw <- icc(pharyngealsNasIRRCast[32:37],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver3oo3v <- icc(pharyngealsNasIRRCast[38:43],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver3oo3w <- icc(pharyngealsNasIRRCast[44:49],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver3oonw <- icc(pharyngealsNasIRRCast[50:55],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver7aadw <- icc(pharyngealsNasIRRCast[56:61],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver7eefv <- icc(pharyngealsNasIRRCast[62:67],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver7eefw <- icc(pharyngealsNasIRRCast[68:73],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver7enw <- icc(pharyngealsNasIRRCast[74:79],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver7ookw <- icc(pharyngealsNasIRRCast[80:85],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAver7oomw <- icc(pharyngealsNasIRRCast[86:91],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverbaa3w <- icc(pharyngealsNasIRRCast[92:97],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverbeetw <- icc(pharyngealsNasIRRCast[98:103],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverbii3v <- icc(pharyngealsNasIRRCast[104:109],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverbii3w <- icc(pharyngealsNasIRRCast[110:115],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverbooshw <- icc(pharyngealsNasIRRCast[116:121],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverdaasw <- icc(pharyngealsNasIRRCast[122:127],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverdiinw <- icc(pharyngealsNasIRRCast[128:133],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverdjuu3v <- icc(pharyngealsNasIRRCast[134:139],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverdjuu3w <- icc(pharyngealsNasIRRCast[140:145],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverdoomv <- icc(pharyngealsNasIRRCast[146:151],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverdoomw <- icc(pharyngealsNasIRRCast[152:157],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverloo7w <- icc(pharyngealsNasIRRCast[158:163],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvermaatv <- icc(pharyngealsNasIRRCast[164:169],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvermaatw <- icc(pharyngealsNasIRRCast[170:175],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvermoozw <- icc(pharyngealsNasIRRCast[176:181],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvermuunw <- icc(pharyngealsNasIRRCast[182:187],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvermuusw <- icc(pharyngealsNasIRRCast[188:193],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvernaa7v <- icc(pharyngealsNasIRRCast[194:199],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvernaa7w <- icc(pharyngealsNasIRRCast[200:205],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvernaamv <- icc(pharyngealsNasIRRCast[206:211],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvernaamw <- icc(pharyngealsNasIRRCast[212:217],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvernoo3v <- icc(pharyngealsNasIRRCast[218:223],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvernoo3w <- icc(pharyngealsNasIRRCast[224:229],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvernoomw <- icc(pharyngealsNasIRRCast[230:235],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvernuu7v <- icc(pharyngealsNasIRRCast[236:241],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAvernuu7w <- icc(pharyngealsNasIRRCast[242:247],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAversaamv <- icc(pharyngealsNasIRRCast[248:253],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAversaamw <- icc(pharyngealsNasIRRCast[254:259],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverzaa7w <- icc(pharyngealsNasIRRCast[260:265],model="twoway",type="agreement",unit="average")
NasICCRatingAgreeWordAverzii7w <- icc(pharyngealsNasIRRCast[266:271],model="twoway",type="agreement",unit="average")

```

### Getting coefficients
Below, we use a a modified script that will allow us to get all coefficients from the models (thanks to Bodo Winter for sharing).

```{r}
###########################################
#getting coefficients # Adapted from Bodo Winter
###########################################

## Get character vectors with all model names for full and null models:

all_models <- grep('ICCRating', ls(), value = T)

## Create an empty data frame to be filled with results from likelihood ratio tests:

resultsICC <- data.frame(Type = character(length(all_models)),
                              ICCVal = numeric(length(all_models)),
                              lowerConfInt = numeric(length(all_models)),
                              upperConfInt = numeric(length(all_models)),
                              pValue = numeric(length(all_models)),
                         stringsAsFactors=FALSE)

## Loop through model names and append do data frame:

for (i in 1:length(all_models)) {
  this_full <- get(all_models[i])
  
  resultsICC[i, ]$Type <- this_full$type
  resultsICC[i, ]$ICCVal <- this_full$value
  resultsICC[i, ]$lowerConfInt <- this_full$lbound
  resultsICC[i, ]$upperConfInt <- this_full$ubound
  resultsICC[i, ]$pValue <- this_full$p.value
}
rownames(resultsICC) <- gsub('ICCRating', 'ICCRating', all_models)

write.csv(resultsICC, file = "PhoneticaResultsICCRating.csv")

```

### Reading and subsetting the ICC data
We have done some minor changes to the saved data-frame in terms of names, etc.. and so we read the data-frame again.
We start by subsetting the data and creating four new data-frames for each of the VQ and Nasalisation experiments and the consistency and agreement. We then change factor levels.

```{r}
PhoneticaICCRatingWord <- read.csv("PhoneticaICCRatingWord.csv")

# some subsetting
PhoneticaICCRatingWordNasAgree <- PhoneticaICCRatingWord[which(PhoneticaICCRatingWord$experiment =='Nasalisation'
                                                          & PhoneticaICCRatingWord$Type == 'agreement'),]

PhoneticaICCRatingWordNasCons <- PhoneticaICCRatingWord[which(PhoneticaICCRatingWord$experiment =='Nasalisation'
                                                               & PhoneticaICCRatingWord$Type == 'consistency'),]


PhoneticaICCRatingWordVQAgree <- PhoneticaICCRatingWord[which(PhoneticaICCRatingWord$experiment =='Voice Quality'
                                                               & PhoneticaICCRatingWord$Type == 'agreement'),]

PhoneticaICCRatingWordVQCons <- PhoneticaICCRatingWord[which(PhoneticaICCRatingWord$experiment =='Voice Quality'
                                                              & PhoneticaICCRatingWord$Type == 'consistency'),]

### below is to be used to change levels of factor after subsetting

PhoneticaICCRatingWordNasAgree$experiment <- factor(PhoneticaICCRatingWordNasAgree$experiment)
PhoneticaICCRatingWordNasAgree$Type <- factor(PhoneticaICCRatingWordNasAgree$Type)


PhoneticaICCRatingWordNasCons$experiment <- factor(PhoneticaICCRatingWordNasCons$experiment)
PhoneticaICCRatingWordNasCons$Type <- factor(PhoneticaICCRatingWordNasCons$Type)


PhoneticaICCRatingWordVQAgree$experiment <- factor(PhoneticaICCRatingWordVQAgree$experiment)
PhoneticaICCRatingWordVQAgree$Type <- factor(PhoneticaICCRatingWordVQAgree$Type)


PhoneticaICCRatingWordVQCons$experiment <- factor(PhoneticaICCRatingWordVQCons$experiment)
PhoneticaICCRatingWordVQCons$Type <- factor(PhoneticaICCRatingWordVQCons$Type)

```


### Figures
Below we change the order of the four data-frame according to the ICC value from lowest to highest. We then reorder the factor level "context". Then we change the name of each of the words into IPA (International Phonetic Alphabet). We create a new ordered factor and then we draw the figure.

#### Voice Quality (VQ)
We start with the first experiment on Voice Quality. Overall, the raters reported difficulties with their ratings of the voice quality experiment, mostly in deciding on the third category "tense". This showed in their consistency and agreements

##### Consistency
We start with the consistency

###### Reordering and changing names
```{r}
## Consistency voice quality 

ord.reRatingConsVQ <- PhoneticaICCRatingWordVQCons[order(PhoneticaICCRatingWordVQCons$ICCVal),]
rownames(ord.reRatingConsVQ) <- NULL
ord.reRatingConsVQ$context <- factor(ord.reRatingConsVQ$context)

ord.reRatingConsVQ$context <- as.character(ord.reRatingConsVQ$context)
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "7aad-w"] <- "\u0127a:d"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "7eef-w"] <- "\u0127e:f"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "7eef-v"] <- "\u0127e:f-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "7en-w"] <- "\u0127enn"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "7ook-w"] <- "\u0127o:k"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "7oom-w"] <- "\u0127o:m"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "loo7-w"] <- "lo:\u0127"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "naa7-w"] <- "na:\u0127"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "naa7-v"] <- "na:\u0127-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "nuu7-w"] <- "nu:\u0127"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "nuu7-v"] <- "nu:\u0127-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "zaa7-w"] <- "za:\u0127"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "zii7-w"] <- "zi:\u0127"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "3aaf-w"] <- "ʕa:f"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "3aam-w"] <- "ʕa:m"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "3eeb-w"] <- "ʕe:b"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "3eeb-v"] <- "ʕe:b-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "3een-w"] <- "ʕe:n"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "3iish-w"] <- "ʕa:ʃ"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "3oo3-w"] <- "ʕo:ʕ"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "3oo3-v"] <- "ʕo:ʕ-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "3oon-w"] <- "ʕo:n"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "baa3-w"] <- "ba:ʕ"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "bii3-w"] <- "bi:ʕ"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "bii3-v"] <- "bi:ʕ-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "boosh-w"] <- "bo:ʃ"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "djuu3-w"] <- "d͡ʒu:ʕ"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "djuu3-v"] <- "d͡ʒu:ʕ-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "noo3-w"] <- "no:ʕ"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "noo3-v"] <- "no:ʕ-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "beet-w"] <- "be:t"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "daas-w"] <- "da:s"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "diin-w"] <- "di:n"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "doom-w"] <- "do:m"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "doom-v"] <- "do:m-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "beet-w"] <- "be:t"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "maat-w"] <- "ma:t"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "maat-v"] <- "ma:t-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "mooz-w"] <- "mo:z"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "muun-w"] <- "mu:n"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "muus-w"] <- "mu:s"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "naam-w"] <- "na:m"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "naam-v"] <- "na:m-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "noom-w"] <- "no:m"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "saam-w"] <- "sa:m"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "saam-v"] <- "sa:m-v"
ord.reRatingConsVQ$context[ord.reRatingConsVQ$context == "beet-w"] <- "be:t"
ord.reRatingConsVQ$context <- as.factor(ord.reRatingConsVQ$context)
#
ord.reRatingConsVQ$context <- as.vector(ord.reRatingConsVQ$context) #get rid of factors
ord.reRatingConsVQ$context = factor(ord.reRatingConsVQ$context,ord.reRatingConsVQ$context) #add ordered factors back


ord.reRatingConsVQ

```

###### Drawing the IRR figure
We use the code below to draw the ICC with confidence intervals for the consistency in rating voice quality. 
```{r fig.width=9, fig.height=5}

## code below allows to draw the figure
par(oma = c(0, 0, 0, 0))
plot(1:45, ord.reRatingConsVQ$ICCVal, axes=FALSE, ylim=c(-3,1),
     ylab="ICC. 95% CI", xlab="",cex=2,cex.lab=1.5,cex.main=1.5,
     main="Consistency ICC for voice quality",cex.axis=1.5)
axis(2,cex.lab=2,cex.axis=1.5)
axis(1,at=1:45, labels = levels(ord.reRatingConsVQ$context), 
     las=2,cex.lab=1.5,cex.axis=1.5)
for(i in 1:45) segments(i,ord.reRatingConsVQ$lowerConfInt[i],
                        i,ord.reRatingConsVQ$upperConfInt[i])

abline(h = 0, lty=2)
abline(v = 32.5, lty=2)

```

As can be seen from the figure above, the rater were generally consistent in their ratings as all the words, minus the first 6 received positive ratings. The ratings for the last 12 words showed a consistently statistically significant positive rating.

##### Agreement
We move to the agreement between raters

###### Reordering and changing names
```{r}
## Agreement Voice Quality 

ord.reRatingAgreeVQ <- PhoneticaICCRatingWordVQAgree[order(PhoneticaICCRatingWordVQAgree$ICCVal),]
rownames(ord.reRatingAgreeVQ) <- NULL
ord.reRatingAgreeVQ$context <- factor(ord.reRatingAgreeVQ$context)

ord.reRatingAgreeVQ$context <- as.character(ord.reRatingAgreeVQ$context)
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "7aad-w"] <- "\u0127a:d"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "7eef-w"] <- "\u0127e:f"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "7eef-v"] <- "\u0127e:f-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "7en-w"] <- "\u0127enn"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "7ook-w"] <- "\u0127o:k"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "7oom-w"] <- "\u0127o:m"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "loo7-w"] <- "lo:\u0127"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "naa7-w"] <- "na:\u0127"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "naa7-v"] <- "na:\u0127-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "nuu7-w"] <- "nu:\u0127"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "nuu7-v"] <- "nu:\u0127-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "zaa7-w"] <- "za:\u0127"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "zii7-w"] <- "zi:\u0127"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "3aaf-w"] <- "ʕa:f"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "3aam-w"] <- "ʕa:m"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "3eeb-w"] <- "ʕe:b"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "3eeb-v"] <- "ʕe:b-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "3een-w"] <- "ʕe:n"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "3iish-w"] <- "ʕa:ʃ"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "3oo3-w"] <- "ʕo:ʕ"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "3oo3-v"] <- "ʕo:ʕ-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "3oon-w"] <- "ʕo:n"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "baa3-w"] <- "ba:ʕ"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "bii3-w"] <- "bi:ʕ"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "bii3-v"] <- "bi:ʕ-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "boosh-w"] <- "bo:ʃ"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "djuu3-w"] <- "d͡ʒu:ʕ"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "djuu3-v"] <- "d͡ʒu:ʕ-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "noo3-w"] <- "no:ʕ"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "noo3-v"] <- "no:ʕ-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "beet-w"] <- "be:t"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "daas-w"] <- "da:s"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "diin-w"] <- "di:n"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "doom-w"] <- "do:m"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "doom-v"] <- "do:m-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "beet-w"] <- "be:t"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "maat-w"] <- "ma:t"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "maat-v"] <- "ma:t-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "mooz-w"] <- "mo:z"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "muun-w"] <- "mu:n"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "muus-w"] <- "mu:s"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "naam-w"] <- "na:m"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "naam-v"] <- "na:m-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "noom-w"] <- "no:m"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "saam-w"] <- "sa:m"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "saam-v"] <- "sa:m-v"
ord.reRatingAgreeVQ$context[ord.reRatingAgreeVQ$context == "beet-w"] <- "be:t"
ord.reRatingAgreeVQ$context <- as.factor(ord.reRatingAgreeVQ$context)


ord.reRatingAgreeVQ$context <- as.vector(ord.reRatingAgreeVQ$context) #get rid of factors
ord.reRatingAgreeVQ$context = factor(ord.reRatingAgreeVQ$context,ord.reRatingAgreeVQ$context) #add ordered factors back

ord.reRatingAgreeVQ

```

###### Drawing the IRR figure

```{r fig.width=9, fig.height=5}
plot(1:45, ord.reRatingAgreeVQ$ICCVal, axes=FALSE, ylim=c(-1,1),
     ylab="ICC. 95% CI", xlab="",cex=2,cex.lab=1.5,cex.main=1.5,
     main="Agreement ICC for voice quality",cex.axis=1.5)
axis(2,cex.lab=2,cex.axis=1.5)
axis(1,at=1:45, labels = levels(ord.reRatingAgreeVQ$context), 
     las=2,cex.lab=1.5,cex.axis=1.5)
for(i in 1:45) segments(i,ord.reRatingAgreeVQ$lowerConfInt[i],
                        i,ord.reRatingAgreeVQ$upperConfInt[i])
abline(h = 0, lty=2)
abline(v = 33.5, lty=2)
```
As with the consistency above, the raters agreed among themselves on the majority of words as these received positive ICCs. The last 12 again received an agreement in ratings that was statistically significant. Some words containing pharyngeals, and specifically the voiced pharyngeal, receive positive ratings.

#### Nasalisation
We move to the second experiment on Nasalisation. Overall, this experiment was slightly easier for the raters as evidenced by the consistency and agreement in their ICC scores.

##### Consistency
We start with the consistency

###### Reordering and changing names

```{r}
## Consistency nasalisation 

ord.reRatingConsNas <- PhoneticaICCRatingWordNasCons[order(PhoneticaICCRatingWordNasCons$ICCVal),]
rownames(ord.reRatingConsNas) <- NULL
ord.reRatingConsNas$context <- factor(ord.reRatingConsNas$context)

ord.reRatingConsNas$context <- as.character(ord.reRatingConsNas$context)
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "7aad-w"] <- "\u0127a:d"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "7eef-w"] <- "\u0127e:f"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "7eef-v"] <- "\u0127e:f-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "7en-w"] <- "\u0127enn"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "7ook-w"] <- "\u0127o:k"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "7oom-w"] <- "\u0127o:m"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "loo7-w"] <- "lo:\u0127"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "naa7-w"] <- "na:\u0127"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "naa7-v"] <- "na:\u0127-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "nuu7-w"] <- "nu:\u0127"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "nuu7-v"] <- "nu:\u0127-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "zaa7-w"] <- "za:\u0127"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "zii7-w"] <- "zi:\u0127"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "3aaf-w"] <- "ʕa:f"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "3aam-w"] <- "ʕa:m"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "3eeb-w"] <- "ʕe:b"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "3eeb-v"] <- "ʕe:b-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "3een-w"] <- "ʕe:n"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "3iish-w"] <- "ʕa:ʃ"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "3oo3-w"] <- "ʕo:ʕ"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "3oo3-v"] <- "ʕo:ʕ-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "3oon-w"] <- "ʕo:n"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "baa3-w"] <- "ba:ʕ"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "bii3-w"] <- "bi:ʕ"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "bii3-v"] <- "bi:ʕ-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "boosh-w"] <- "bo:ʃ"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "djuu3-w"] <- "d͡ʒu:ʕ"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "djuu3-v"] <- "d͡ʒu:ʕ-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "noo3-w"] <- "no:ʕ"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "noo3-v"] <- "no:ʕ-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "beet-w"] <- "be:t"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "daas-w"] <- "da:s"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "diin-w"] <- "di:n"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "doom-w"] <- "do:m"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "doom-v"] <- "do:m-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "beet-w"] <- "be:t"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "maat-w"] <- "ma:t"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "maat-v"] <- "ma:t-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "mooz-w"] <- "mo:z"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "muun-w"] <- "mu:n"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "muus-w"] <- "mu:s"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "naam-w"] <- "na:m"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "naam-v"] <- "na:m-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "noom-w"] <- "no:m"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "saam-w"] <- "sa:m"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "saam-v"] <- "sa:m-v"
ord.reRatingConsNas$context[ord.reRatingConsNas$context == "beet-w"] <- "be:t"
ord.reRatingConsNas$context <- as.factor(ord.reRatingConsNas$context)


ord.reRatingConsNas$context <- as.vector(ord.reRatingConsNas$context) #get rid of factors
ord.reRatingConsNas$context = factor(ord.reRatingConsNas$context,ord.reRatingConsNas$context) #add ordered factors back


ord.reRatingConsNas

```

###### Drawing the IRR figure

```{r fig.width=9, fig.height=5}
par(oma = c(0, 0, 0, 0))
plot(1:45, ord.reRatingConsNas$ICCVal, axes=FALSE, ylim=c(-1,1),
     ylab="ICC. 95% CI", xlab="",cex=2,cex.lab=1.5,cex.main=1.5,
     main="Consistency ICC for nasalisation",cex.axis=1.5)
axis(2,cex.lab=2,cex.axis=1.5)
axis(1,at=1:45, labels = levels(ord.reRatingConsNas$context), 
     las=2,cex.lab=1.5,cex.axis=1.5)
for(i in 1:45) segments(i,ord.reRatingConsNas$lowerConfInt[i],
                        i,ord.reRatingConsNas$upperConfInt[i])

abline(h = 0, lty=2)
abline(v = 21.5, lty=2)
```
The nasalisation experiment was somehow "easier" to raters as they were always consistent in their ratings with only two words received negative ICCs and nearly half of the words showing a statistically significant positive rating.

##### Agreement
We move to the agreement

###### Reordering and changing names

```{r}

## Agreement nasalisation 

ord.reRatingAgreeNas <- PhoneticaICCRatingWordNasAgree[order(PhoneticaICCRatingWordNasAgree$ICCVal),]
rownames(ord.reRatingAgreeNas) <- NULL
ord.reRatingAgreeNas$context <- factor(ord.reRatingAgreeNas$context)

ord.reRatingAgreeNas$context <- as.character(ord.reRatingAgreeNas$context)
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "7aad-w"] <- "\u0127a:d"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "7eef-w"] <- "\u0127e:f"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "7eef-v"] <- "\u0127e:f-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "7en-w"] <- "\u0127enn"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "7ook-w"] <- "\u0127o:k"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "7oom-w"] <- "\u0127o:m"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "loo7-w"] <- "lo:\u0127"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "naa7-w"] <- "na:\u0127"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "naa7-v"] <- "na:\u0127-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "nuu7-w"] <- "nu:\u0127"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "nuu7-v"] <- "nu:\u0127-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "zaa7-w"] <- "za:\u0127"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "zii7-w"] <- "zi:\u0127"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "3aaf-w"] <- "ʕa:f"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "3aam-w"] <- "ʕa:m"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "3eeb-w"] <- "ʕe:b"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "3eeb-v"] <- "ʕe:b-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "3een-w"] <- "ʕe:n"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "3iish-w"] <- "ʕa:ʃ"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "3oo3-w"] <- "ʕo:ʕ"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "3oo3-v"] <- "ʕo:ʕ-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "3oon-w"] <- "ʕo:n"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "baa3-w"] <- "ba:ʕ"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "bii3-w"] <- "bi:ʕ"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "bii3-v"] <- "bi:ʕ-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "boosh-w"] <- "bo:ʃ"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "djuu3-w"] <- "d͡ʒu:ʕ"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "djuu3-v"] <- "d͡ʒu:ʕ-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "noo3-w"] <- "no:ʕ"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "noo3-v"] <- "no:ʕ-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "beet-w"] <- "be:t"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "daas-w"] <- "da:s"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "diin-w"] <- "di:n"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "doom-w"] <- "do:m"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "doom-v"] <- "do:m-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "beet-w"] <- "be:t"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "maat-w"] <- "ma:t"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "maat-v"] <- "ma:t-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "mooz-w"] <- "mo:z"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "muun-w"] <- "mu:n"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "muus-w"] <- "mu:s"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "naam-w"] <- "na:m"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "naam-v"] <- "na:m-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "noom-w"] <- "no:m"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "saam-w"] <- "sa:m"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "saam-v"] <- "sa:m-v"
ord.reRatingAgreeNas$context[ord.reRatingAgreeNas$context == "beet-w"] <- "be:t"
ord.reRatingAgreeNas$context <- as.factor(ord.reRatingAgreeNas$context)


ord.reRatingAgreeNas$context <- as.vector(ord.reRatingAgreeNas$context) #get rid of factors
ord.reRatingAgreeNas$context = factor(ord.reRatingAgreeNas$context,ord.reRatingAgreeNas$context) #add ordered factors back


ord.reRatingAgreeNas

```

###### Drawing the IRR figure

```{r fig.width=9, fig.height=5}
par(oma = c(0, 0, 0, 0))
plot(1:45, ord.reRatingAgreeNas$ICCVal, axes=FALSE, ylim=c(-1,1),
     ylab="ICC. 95% CI", xlab="",cex=2,cex.lab=1.5,cex.main=1.5,
     main="Agreement ICC for nasalisation",cex.axis=1.5)
axis(2,cex.lab=2,cex.axis=1.5)
axis(1,at=1:45, labels = levels(ord.reRatingAgreeNas$context), 
     las=2,cex.lab=1.5,cex.axis=1.5)
for(i in 1:45) segments(i,ord.reRatingAgreeNas$lowerConfInt[i],
                        i,ord.reRatingAgreeNas$upperConfInt[i])

abline(h = 0, lty=2)
abline(v = 20.5, lty=2)
```
Again, only the last two words showed a negative agreement between raters, with nearly half of the words receiving a statistically significant positive rating. And as expected, most of the words having nasal sounds sounds, received the highest ratings, with some having both nasals and pharyngeals receiving positive ratings.

### Conclusion
Given the level of variance observed in the ICC scores for both consistency and agreements, it was necessary to use a statistical technique that takes into account this. It is possible that the raters were variables in some of their responses due to variable productions by the producing subjects and/or by the inherent differences between the raters.  
Below we report on the results of the Cumulative Logit Mixed Model with subjects, raters and items as random effects.

## Cumulative Logit Mixed Models (clmm)
The data were analysed using Cumulative Link Mixed Models (CLMM) using the "ordinal" package. 

Given the structure of the data, we used a random effects structure to account for the design. A crossed random effects structure (for the producing subjects, the raters and the items) was used. A by-rater random slope for Context was used as it improved the model fit compared to a model with it. 

Our outcome is the response entered as a factor (3 for Voice Quality; 5 for Nasalisation). Our predictor was Context with the reference level being set to "Isolation". 

We compared various combinations, e.g., by subject and item random slopes; interactions with vowel, etc. and all these did not improve the model fit. Hence the models reported below are the optimal ones 

### Models

#### Loading packages
Start by loading packages and install those that are not installed

```{r warning=FALSE, message=FALSE, error=FALSE}
requiredPackages = c('ordinal')
for(p in requiredPackages){
  if(!require(p,character.only = TRUE)) install.packages(p)
  library(p,character.only = TRUE)
}
```

#### Reading the data
We then read in the data and look at the structure to verify the class of each column.
```{r warning=FALSE, message=FALSE, error=FALSE}
percVQDF <- read.csv("percVQDF.csv")
percNasDF <- read.csv("percNasDF.csv")
str(percVQDF)
str(percNasDF)
```

#### Changing a few things
We are changing a few things in both data-frames

1. Changing type of "response" to factor to suite the Cumulative Logit Mixed Model

2. Checking the levels of Context and changing the reference category to "Isolation"

3. Creating a new variable "ContextIPA" that contains the IPA symbols to be plotted in the figures below

```{r warning=FALSE, message=FALSE, error=FALSE}
# factor level
percVQDF$Response <- as.factor(percVQDF$Response)
percNasDF$Response <- as.factor(percNasDF$Response)


# Changing the reference level
levels(percVQDF$Context)
percVQDF$Context <- relevel(percVQDF$Context, ref="isolation")
levels(percNasDF$Context)
percNasDF$Context <- relevel(percNasDF$Context, ref="isolation")

# Creating a new variable "ContextIPA"
## for Voice Quallity
percVQDF$ContextIPA <- percVQDF$Context
percVQDF$ContextIPA <- as.character(percVQDF$ContextIPA)
percVQDF$ContextIPA[percVQDF$ContextIPA == "7-o"] <- "\u0127-o"
percVQDF$ContextIPA[percVQDF$ContextIPA == "o-7"] <- "o-\u0127"
percVQDF$ContextIPA[percVQDF$ContextIPA == "7-n"] <- "\u0127-n"
percVQDF$ContextIPA[percVQDF$ContextIPA == "n-7"] <- "n-\u0127"
percVQDF$ContextIPA[percVQDF$ContextIPA == "3-o"] <- "ʕ-o"
percVQDF$ContextIPA[percVQDF$ContextIPA == "o-3"] <- "o-ʕ"
percVQDF$ContextIPA[percVQDF$ContextIPA == "3-n"] <- "ʕ-n"
percVQDF$ContextIPA[percVQDF$ContextIPA == "n-3"] <- "n-ʕ"
percVQDF$ContextIPA[percVQDF$ContextIPA == "3--3"] <- "ʕ-ʕ"
percVQDF$ContextIPA[percVQDF$ContextIPA == "isolation"] <- "Isolation"
percVQDF$ContextIPA <- as.factor(percVQDF$ContextIPA)
percVQDF$ContextIPA <- relevel(percVQDF$ContextIPA, ref="Isolation")
levels(percVQDF$ContextIPA)
# reordering levels of predictor
percVQDF$Context <- factor(percVQDF$Context, levels = c("isolation","7-n","7-o","n-7","n-n","n-o","n-3","o-7","o-n","o-o","o-3","3-n","3-o","3--3"))
percVQDF$ContextIPA <- factor(percVQDF$ContextIPA, levels = c("Isolation","\u0127-n","\u0127-o","n-\u0127","n-n","n-o","n-ʕ","o-\u0127","o-n","o-o","o-ʕ","ʕ-n","ʕ-o","ʕ-ʕ"))
levels(percVQDF$Context)
levels(percVQDF$ContextIPA)

## for Nasalisation
percNasDF$ContextIPA <- percNasDF$Context
percNasDF$ContextIPA <- as.character(percNasDF$ContextIPA)
percNasDF$ContextIPA[percNasDF$ContextIPA == "7-o"] <- "\u0127-o"
percNasDF$ContextIPA[percNasDF$ContextIPA == "o-7"] <- "o-\u0127"
percNasDF$ContextIPA[percNasDF$ContextIPA == "7-n"] <- "\u0127-n"
percNasDF$ContextIPA[percNasDF$ContextIPA == "n-7"] <- "n-\u0127"
percNasDF$ContextIPA[percNasDF$ContextIPA == "3-o"] <- "ʕ-o"
percNasDF$ContextIPA[percNasDF$ContextIPA == "o-3"] <- "o-ʕ"
percNasDF$ContextIPA[percNasDF$ContextIPA == "3-n"] <- "ʕ-n"
percNasDF$ContextIPA[percNasDF$ContextIPA == "n-3"] <- "n-ʕ"
percNasDF$ContextIPA[percNasDF$ContextIPA == "3--3"] <- "ʕ-ʕ"
percNasDF$ContextIPA[percNasDF$ContextIPA == "isolation"] <- "Isolation"
percNasDF$ContextIPA <- as.factor(percNasDF$ContextIPA)
percNasDF$ContextIPA <- relevel(percNasDF$ContextIPA, ref="Isolation")
levels(percNasDF$ContextIPA)

# reordering levels of predictor
percNasDF$Context <- factor(percNasDF$Context, levels = c("isolation","7-n","7-o","n-7","n-n","n-o","n-3","o-7","o-n","o-o","o-3","3-n","3-o","3--3"))
percNasDF$ContextIPA <- factor(percNasDF$ContextIPA, levels = c("Isolation","\u0127-n","\u0127-o","n-\u0127","n-n","n-o","n-ʕ","o-\u0127","o-n","o-o","o-ʕ","ʕ-n","ʕ-o","ʕ-ʕ"))
levels(percNasDF$Context)
levels(percNasDF$ContextIPA)
```

### Model specifications
As can be seem from the results of system time, it took roungly one hour to run the first model with a 4-Core machine running Microsoft R Open version 3.5.0 that ran the model using parallel computing (total around 4:30 hours). With the base R, it may take at least double this time to run, if not more given that with base R, only one core is used (unless specifically using parallel computing).

#### Voice Quality (VQ) 
Below is our model specification for the full model that improved the model fit (we have tried various combinations, including by-Subject and by-Item random slopes for context; adding vowel as a fixed and random slope; consonant*vowel interactions, etc. However all these models either did not converge or did not improve the model fit compared to our model below)

##### Full model
```{r warning=FALSE, message=FALSE, error=FALSE}
system.time(fullCLMMVQSlope <- clmm(Response ~ Context + (1|Subject)+(1|Item)+(Context|Rater), data=percVQDF))
```

##### Null model

```{r}
system.time(fullCLMMVQNull <- clmm(Response ~ 1 + (1|Subject)+(1|Item)+(Context|Rater), data=percVQDF))

```

#### Nasalisation
Below is our model specification for the full model that improved the model fit (we have tried various combinations, including by-Subject and by-Item random slopes for context; adding vowel as a fixed and random slope; consonant*vowel interactions, etc. However all these models either did not converge or did not improve the model fit compared to our model below)

##### Full model
```{r warning=FALSE, message=FALSE, error=FALSE}
system.time(fullCLMMNasSlope <- clmm(Response ~ Context + (1|Subject)+(1|Item)+(Context|Rater), data=percNasDF))
```

##### Null model

```{r}
system.time(fullCLMMNasNull <- clmm(Response ~ 1 + (1|Subject)+(1|Item)+(Context|Rater), data=percNasDF))

```

### Results
The results below are divided into those on Voice Quality (VQ) followed by those on Nasalisation.  

#### Voice Quality (VQ)

##### Model comparisons

Through model comparison between the optimal model and the null model (aka Intercept only model), the results show that using our optimal model improved the model fit. 
```{r warning=FALSE, message=FALSE, error=FALSE}
anova(fullCLMMVQNull,fullCLMMVQSlope)
```

##### Summary
Next the summary presented below shows the results for each of the coefficients (minus the Intercept) and of the thresholds (1|2 and 2|3). 

Starting with the threshlods, the results show that overall, a decrease in ratings of tense voice quality is obtained for ratings between 1 and 2, which increases with ratings between 2 to 3. The coefficients of the 12 levels of the fixed effect "Context" show either negative or positive values. The negative ones indicate that the ratings of "tense" are decreased in these contexts (e.g., o-o or n-o) which obtained lower betas compared to the reference value "Isolation". All positive values are associated with increase in ratings of "tense" voice quality, though some are not statistically significant.

```{r warning=FALSE, message=FALSE, error=FALSE}
summary(fullCLMMVQSlope)
```

##### Figure
The summary of the results showed some statistically significant coefficients that displayed an association with increased ratings of "tense". To allow us to visualise the ratings, we use the script below, which is a modified version of two scripts (see references in article). 

The "beta" is the coefficient for each level of the fixed effect "Context" and "Theta" is the coefficient for each threshold of the ratings "Response", 1|2, 2|3, etc...


```{r warning=FALSE, message=FALSE, error=FALSE}
## below changes the margins
par(oma=c(1, 0, 0, 3),mgp=c(2, 1, 0))
xlimVQ = c(min(fullCLMMVQSlope$beta), max(fullCLMMVQSlope$beta))
ylimVQ = c(0,1)
plot(0,0,xlim=xlimVQ, ylim=ylimVQ, type="n", ylab=expression(Probability), xlab="", xaxt = "n",main="Predicted curves - Voice Quality",cex=2,cex.lab=1.5,cex.main=1.5,cex.axis=1.5)
axis(side = 1, at = c(0,fullCLMMVQSlope$beta),labels = levels(percVQDF$ContextIPA), las=2,cex=2,cex.lab=1.5,cex.axis=1.5)
xsVQ = seq(xlimVQ[1], xlimVQ[2], length.out=100)
lines(xsVQ, plogis(fullCLMMVQSlope$Theta[1] - xsVQ), col='black')
lines(xsVQ, plogis(fullCLMMVQSlope$Theta[2] - xsVQ)-plogis(fullCLMMVQSlope$Theta[1] - xsVQ), col='red')
lines(xsVQ, 1- (plogis(fullCLMMVQSlope$Theta[2] - xsVQ)), col='blue')
abline(v=c(0,fullCLMMVQSlope$beta),lty=3)
abline(h=0, lty="dashed")
abline(h=1, lty="dashed")
legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1, col=c("black", "red", "blue"), 
       legend=c("Breathy", "Modal", "Tense"),cex=0.75)
```

The figure above show that ratings of "Breathy" received less than 15% specifically and this was mainly either in the "Isolation" context or contexts with nasal consonants. This same group showed increase in ratings of "Modal" voice at a rate close to 75%. Ratings associated with the "Tense" voice increased steadily from this first group until reaching the pharyngeal contexts, with the highest ratings of "Tense" being present in /ʕ-ʕ/ context at a rate of 80%, with the remaining 20% being associated with "Modal" voice

#### Nasalisation

##### Model comparisons

With Nasalisation, model comparisons showed again an improvement of the model fit with our optimal model. 
```{r warning=FALSE, message=FALSE, error=FALSE}
anova(fullCLMMNasNull,fullCLMMNasSlope)
```

##### Summary

The summary of the model showed again a steady increase in ratings associated with nasal from 1 to 5. Ratings of 1|2 and 2|3 are not statistically significant; those from 3 to 5 are statistically and significantly associated with nasalisation. The coefficients of the 12 levels of the fixed effect are either negative, i.e., not associated with ratings of nasalisation, specifically when an oral context is in initial position, or when a pharyngeal is associated with an oral context. When pharyngeals are associated with nasals, ratings of nasalisation are increased.


```{r warning=FALSE, message=FALSE, error=FALSE}
summary(fullCLMMNasSlope)
```


##### Figure
```{r warning=FALSE, message=FALSE, error=FALSE}
## below changes the margins
par(oma=c(1, 0, 0, 3),mgp=c(2, 1, 0))
xlimNas = c(min(fullCLMMNasSlope$beta), max(fullCLMMNasSlope$beta))
ylimNas = c(0,1)
plot(0,0,xlim=xlimNas, ylim=ylimNas, type="n", ylab=expression(Probability), xlab="", xaxt = "n",main="Predicted curves - Nasalisation",cex=2,cex.lab=1.5,cex.main=1.5,cex.axis=1.5)
axis(side = 1, at = c(0,fullCLMMNasSlope$beta),labels = levels(percNasDF$ContextIPA), las=2,cex=2,cex.lab=1.5,cex.axis=1.5)
xsNas = seq(xlimNas[1], xlimNas[2], length.out=100)
lines(xsNas, plogis(fullCLMMNasSlope$Theta[1] - xsNas), col='black')
lines(xsNas, plogis(fullCLMMNasSlope$Theta[2] - xsNas)-plogis(fullCLMMNasSlope$Theta[1] - xsNas), col='red')
lines(xsNas, plogis(fullCLMMNasSlope$Theta[3] - xsNas)-plogis(fullCLMMNasSlope$Theta[2] - xsNas), col='green')
lines(xsNas, plogis(fullCLMMNasSlope$Theta[4] - xsNas)-plogis(fullCLMMNasSlope$Theta[3] - xsNas), col='orange')
lines(xsNas, 1-(plogis(fullCLMMNasSlope$Theta[4] - xsNas)), col='blue')
abline(v=c(0,fullCLMMNasSlope$beta),lty=3)
abline(h=0, lty="dashed")
abline(h=1, lty="dashed")
legend(par('usr')[2], par('usr')[4], bty='n', xpd=NA,lty=1, col=c("black", "red", "green", "orange", "blue"), 
       legend=c("Oral", "2", "3", "4", "Nasal"),cex=0.75)
```

The figure above shows how two groups are formed. The first group includes all oral contexts, Isolation and pharyngeals either on their own or with an oral context. The second group include all nasal contexts and those including pharyngeals and nasals. It is interesting to see that the percept of nasality is increased when a pharyngeal is associated with a nasal context; when associated with an oral context, the ratings are mostly "Oral". The context "n-o" and "n-n" received the highest ratings of "Nasal" (4 and 5) with a sum of around 85-90%. This was followed by both "n-ʕ" and "ʕ-n" at a combined rating of around 80%.

### Exploring random effects
This section is not part of the analsyes presented in the article, but are crucial at understanding how the rating experiment worked and how the random factors were crucial in generalising the results. Without including random effects, the model would have provided wrong predictions, given that our subjects, raters and words showed clear variance. This was taken into account in the models presented above.

#### Voice Quality (VQ)

##### Preprocessing data
We start by preprocessing the data. We create data-frames with intercepts and variances of all random effects; the first column is the grouping factor, followed by 5 columns of intercepts, columns 7-11 are the variances.
```{r}
#### 
# 

#getting betas
betasVQ <- as.data.frame(fullCLMMVQSlope$beta)

# getting random effects coeficients and conditional variance
# for subjects
randomsVQSubject <- as.data.frame(ranef(fullCLMMVQSlope, condVar = F)$Subject)
varVQSubject     <- as.data.frame(condVar(fullCLMMVQSlope)$Subject)

dfVQSubject      <- merge(randomsVQSubject, varVQSubject, by ="row.names")
betasVQ
dfVQSubject
subRanefVQSubj <- fullCLMMVQSlope$ranef[46:54]
subcondVarVQSubj <- condVar(fullCLMMVQSlope)$Subject[1]
subcondVarVQSubj <- matrix(as.numeric(unlist(subcondVarVQSubj)),nrow=nrow(subcondVarVQSubj)) 


# getting random effects coeficients and conditional variance
# for raters
randomsVQRater <- as.data.frame(ranef(fullCLMMVQSlope, condVar = F)$Rater)
varVQRater     <- as.data.frame(condVar(fullCLMMVQSlope)$Rater)
dfVQRater      <- merge(randomsVQRater, varVQRater, by ="row.names")
dfVQRater
subRanefVQRater <- fullCLMMVQSlope$ranef[55:60]
subcondVarVQRater <- condVar(fullCLMMVQSlope)$Rater[1]
subcondVarVQRater <- matrix(as.numeric(unlist(subcondVarVQRater)),nrow=nrow(subcondVarVQRater)) 

# getting random effects coeficients and conditional variance
# for words
randomsVQWord <- as.data.frame(ranef(fullCLMMVQSlope, condVar = F)$Item)
varVQWord     <- as.data.frame(condVar(fullCLMMVQSlope)$Item)
dfVQWord      <- merge(randomsVQWord, varVQWord, by ="row.names")
dfVQWord
subRanefVQWord <- fullCLMMVQSlope$ranef[1:45]
subcondVarVQWord <- condVar(fullCLMMVQSlope)$Item[1]
subcondVarVQWord <- matrix(as.numeric(unlist(subcondVarVQWord)),nrow=nrow(subcondVarVQWord)) 

```

We then reorder the random effects by their labels. We also change a few things

```{r}
labelsVQ <- c(levels(percVQDF$Item),levels(percVQDF$Subject),
             levels(percVQDF$Rater))

ord.reLabelVQ <- as.data.frame(fullCLMMVQSlope$ranef[1:60])
ord.reLabelVQ$var <- row.names(fullCLMMVQSlope$ranef[1:60])
ord.reLabelVQ <- merge(labelsVQ,ord.reLabelVQ,by ="row.names")
ord.reLabelVQ

ord.reLabelVQ$Row.names <- as.numeric(ord.reLabelVQ$Row.names)
ord.reLabelVQ <- ord.reLabelVQ[ order(ord.reLabelVQ[,1]), ]
rownames(ord.reLabelVQ) <- NULL

ord.reLabelVQ

ord.reLabelVQWord <- ord.reLabelVQ[1:45,]
ord.reLabelVQSubj <- ord.reLabelVQ[46:54,]
ord.reLabelVQRater <- ord.reLabelVQ[55:60,]
ord.reLabelVQWord$x <- factor(ord.reLabelVQWord$x)
ord.reLabelVQSubj$x <- factor(ord.reLabelVQSubj$x)
ord.reLabelVQRater$x <- factor(ord.reLabelVQRater$x)

#changing items from transliteration to IPA
ord.reLabelVQWord$x <- as.character(ord.reLabelVQWord$x)
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "7aad-w"] <- "\u0127a:d"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "7eef-w"] <- "\u0127e:f"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "7eef-v"] <- "\u0127e:f-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "7en-w"] <- "\u0127enn"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "7ook-w"] <- "\u0127o:k"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "7oom-w"] <- "\u0127o:m"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "loo7-w"] <- "lo:\u0127"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "naa7-w"] <- "na:\u0127"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "naa7-v"] <- "na:\u0127-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "nuu7-w"] <- "nu:\u0127"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "nuu7-v"] <- "nu:\u0127-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "zaa7-w"] <- "za:\u0127"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "zii7-w"] <- "zi:\u0127"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "3aaf-w"] <- "ʕa:f"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "3aam-w"] <- "ʕa:m"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "3eeb-w"] <- "ʕe:b"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "3eeb-v"] <- "ʕe:b-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "3een-w"] <- "ʕe:n"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "3iish-w"] <- "ʕa:ʃ"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "3oo3-w"] <- "ʕo:ʕ"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "3oo3-v"] <- "ʕo:ʕ-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "3oon-w"] <- "ʕo:n"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "baa3-w"] <- "ba:ʕ"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "bii3-w"] <- "bi:ʕ"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "bii3-v"] <- "bi:ʕ-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "boosh-w"] <- "bo:ʃ"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "djuu3-w"] <- "d͡ʒu:ʕ"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "djuu3-v"] <- "d͡ʒu:ʕ-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "noo3-w"] <- "no:ʕ"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "noo3-v"] <- "no:ʕ-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "beet-w"] <- "be:t"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "daas-w"] <- "da:s"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "diin-w"] <- "di:n"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "doom-w"] <- "do:m"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "doom-v"] <- "do:m-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "beet-w"] <- "be:t"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "maat-w"] <- "ma:t"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "maat-v"] <- "ma:t-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "mooz-w"] <- "mo:z"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "muun-w"] <- "mu:n"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "muus-w"] <- "mu:s"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "naam-w"] <- "na:m"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "naam-v"] <- "na:m-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "noom-w"] <- "no:m"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "saam-w"] <- "sa:m"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "saam-v"] <- "sa:m-v"
ord.reLabelVQWord$x[ord.reLabelVQWord$x == "beet-w"] <- "be:t"
ord.reLabelVQWord$x <- as.factor(ord.reLabelVQWord$x)

# reordering
ord.reLabelVQWord <- ord.reLabelVQWord %>% arrange(`fullCLMMVQSlope$ranef[1:60]`)

ord.reLabelVQWord$x <- as.vector(ord.reLabelVQWord$x) #get rid of factors
ord.reLabelVQWord$x = factor(ord.reLabelVQWord$x,ord.reLabelVQWord$x) #add ordered factors back


ord.reLabelVQSubj <- ord.reLabelVQSubj %>% arrange(`fullCLMMVQSlope$ranef[1:60]`)

ord.reLabelVQSubj$x <- as.vector(ord.reLabelVQSubj$x) #get rid of factors
ord.reLabelVQSubj$x = factor(ord.reLabelVQSubj$x,ord.reLabelVQSubj$x) #add ordered factors back


ord.reLabelVQRater <- ord.reLabelVQRater %>% arrange(`fullCLMMVQSlope$ranef[1:60]`)

ord.reLabelVQRater$x <- as.vector(ord.reLabelVQRater$x) #get rid of factors
ord.reLabelVQRater$x = factor(ord.reLabelVQRater$x,ord.reLabelVQRater$x) #add ordered factors back

```


##### Figures

###### Subjects

```{r}
ciVQSubj <- subRanefVQSubj + qnorm(0.975) * sqrt(as.numeric(subcondVarVQSubj)) %o% c(-1, 1)
ord.reVQSubj <- order(subRanefVQSubj)
ciVQSubj <- ciVQSubj[order(subRanefVQSubj),]
plot(subRanefVQSubj[ord.reVQSubj], 1:9, axes=FALSE, xlim=range(ciVQSubj),
     xlab="Subject variation", ylab="",cex=2,cex.lab=2,cex.main=2,
     main="BLUPs Subject VQ",cex.axis=1.5)
axis(1,cex.lab=2,cex.axis=1.5)
axis(2,at=1:9, labels = levels(ord.reLabelVQSubj$x), las=2,cex.lab=2,cex.axis=1.5)
for(i in 1:9) segments(ciVQSubj[i,1], i, ciVQSubj[i, 2],i)
abline(v = 0, lty=2)
```

It is interesting to see that not all subjects (who produced the items) were rated as more or less "tense". The first two subjects (2 and 3) contributed more to the percept of "tense" voice quality whereas speakers 6, 9 and 4, contributed less to this percept.  

###### Raters
```{r}

ciVQRater <- subRanefVQRater + qnorm(0.975) * sqrt(as.numeric(subcondVarVQRater)) %o% c(-1, 1)
ord.reVQRater <- order(subRanefVQRater)
ciVQRater <- ciVQRater[order(subRanefVQRater),]
plot(subRanefVQRater[ord.reVQRater], 1:6, axes=FALSE, xlim=range(ciVQRater),
     xlab="Rater variation", ylab="",cex=2,cex.lab=2,cex.main=2,
     main="BLUPs Rater VQ",cex.axis=1.5)
axis(1,cex.lab=2,cex.axis=1.5)
axis(2,at=1:6, labels = levels(ord.reLabelVQRater$x), las=2,cex.lab=2,cex.axis=1.5)
for(i in 1:6) segments(ciVQRater[i,1], i, ciVQRater[i, 2],i)
abline(v = 0, lty=2)

```

With respect to raters, it is interesting to see that there is a clear split between raters (that was already picked up in the IRR analyses above). The first three (2, 5 and 6) were more likely to provide postive responses to "tense" whereas 1 and 4 (and potentially 3) were more conservative in their responses. R01 is the first author and R02 is the second. This shows that R01 is more conservative in their responses than R02.

###### Items
```{r fig.width=9, fig.height=5}

ciVQWord <- subRanefVQWord + qnorm(0.975) * sqrt(as.numeric(subcondVarVQWord)) %o% c(-1, 1)
ord.reVQWord <- order(subRanefVQWord)
ciVQWord <- ciVQWord[order(subRanefVQWord),]
par(oma = c(0, 1.5, 0, 0))
plot(1:45,subRanefVQWord[ord.reVQWord], axes=FALSE, ylim=range(ciVQWord),
       ylab="Item variation", xlab="",cex=2,cex.lab=2,cex.main=2,
     main="BLUPs Item VQ",cex.axis=1.5)
axis(2,cex.lab=2,cex.axis=1.5)
axis(1,at=1:45, labels = levels(ord.reLabelVQWord$x), las=2,cex.lab=2,cex.axis=1.5)
for(i in 1:45) segments(i, ciVQWord[i,1], i, ciVQWord[i, 2])
abline(h = 0, lty=2)
```

With item variation, not all words contributed positively to the "tense" quality and there is a clear gradation in impact of words on ratings. This is important to take into account of course in our model

#### Nasalisation

##### Preprocessing data
We start by preprocessing the data. We create data-frames with intercepts and variances of all random effects; the first column is the grouping factor, followed by 5 columns of intercepts, columns 7-11 are the variances.
```{r}
#### 
# 

#getting betas
betasNas <- as.data.frame(fullCLMMNasSlope$beta)

# getting random effects coeficients and conditional variance
# for subjects
randomsNasSubject <- as.data.frame(ranef(fullCLMMNasSlope, condVar = F)$Subject)
varNasSubject     <- as.data.frame(condVar(fullCLMMNasSlope)$Subject)

dfNasSubject      <- merge(randomsNasSubject, varNasSubject, by ="row.names")
betasNas
dfNasSubject
subRanefNasSubj <- fullCLMMNasSlope$ranef[46:54]
subcondVarNasSubj <- condVar(fullCLMMNasSlope)$Subject[1]
subcondVarNasSubj <- matrix(as.numeric(unlist(subcondVarNasSubj)),nrow=nrow(subcondVarNasSubj)) 


# getting random effects coeficients and conditional variance
# for raters
randomsNasRater <- as.data.frame(ranef(fullCLMMNasSlope, condVar = F)$Rater)
varNasRater     <- as.data.frame(condVar(fullCLMMNasSlope)$Rater)
dfNasRater      <- merge(randomsNasRater, varNasRater, by ="row.names")
dfNasRater
subRanefNasRater <- fullCLMMNasSlope$ranef[55:60]
subcondVarNasRater <- condVar(fullCLMMNasSlope)$Rater[1]
subcondVarNasRater <- matrix(as.numeric(unlist(subcondVarNasRater)),nrow=nrow(subcondVarNasRater)) 

# getting random effects coeficients and conditional variance
# for words
randomsNasWord <- as.data.frame(ranef(fullCLMMNasSlope, condVar = F)$Item)
varNasWord     <- as.data.frame(condVar(fullCLMMNasSlope)$Item)
dfNasWord      <- merge(randomsNasWord, varNasWord, by ="row.names")
dfNasWord
subRanefNasWord <- fullCLMMNasSlope$ranef[1:45]
subcondVarNasWord <- condVar(fullCLMMNasSlope)$Item[1]
subcondVarNasWord <- matrix(as.numeric(unlist(subcondVarNasWord)),nrow=nrow(subcondVarNasWord)) 

```

We then reorder the random effects by their labels. We also change a few things

```{r}
labelsNas <- c(levels(percNasDF$Item),levels(percNasDF$Subject),levels(percNasDF$Rater))

ord.reLabelNas <- as.data.frame(fullCLMMNasSlope$ranef[1:60])
ord.reLabelNas$var <- row.names(fullCLMMNasSlope$ranef[1:60])
ord.reLabelNas <- merge(labelsNas,ord.reLabelNas,by ="row.names")
ord.reLabelNas

ord.reLabelNas$Row.names <- as.numeric(ord.reLabelNas$Row.names)
ord.reLabelNas <- ord.reLabelNas[ order(ord.reLabelNas[,1]), ]
rownames(ord.reLabelNas) <- NULL

ord.reLabelNas

ord.reLabelNasWord <- ord.reLabelNas[1:45,]
ord.reLabelNasSubj <- ord.reLabelNas[46:54,]
ord.reLabelNasRater <- ord.reLabelNas[55:60,]
ord.reLabelNasWord$x <- factor(ord.reLabelNasWord$x)
ord.reLabelNasSubj$x <- factor(ord.reLabelNasSubj$x)
ord.reLabelNasRater$x <- factor(ord.reLabelNasRater$x)

#changing items from transliteration to IPA
ord.reLabelNasWord$x <- as.character(ord.reLabelNasWord$x)
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "7aad-w"] <- "\u0127a:d"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "7eef-w"] <- "\u0127e:f"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "7eef-v"] <- "\u0127e:f-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "7en-w"] <- "\u0127enn"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "7ook-w"] <- "\u0127o:k"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "7oom-w"] <- "\u0127o:m"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "loo7-w"] <- "lo:\u0127"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "naa7-w"] <- "na:\u0127"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "naa7-v"] <- "na:\u0127-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "nuu7-w"] <- "nu:\u0127"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "nuu7-v"] <- "nu:\u0127-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "zaa7-w"] <- "za:\u0127"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "zii7-w"] <- "zi:\u0127"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "3aaf-w"] <- "ʕa:f"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "3aam-w"] <- "ʕa:m"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "3eeb-w"] <- "ʕe:b"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "3eeb-v"] <- "ʕe:b-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "3een-w"] <- "ʕe:n"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "3iish-w"] <- "ʕa:ʃ"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "3oo3-w"] <- "ʕo:ʕ"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "3oo3-v"] <- "ʕo:ʕ-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "3oon-w"] <- "ʕo:n"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "baa3-w"] <- "ba:ʕ"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "bii3-w"] <- "bi:ʕ"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "bii3-v"] <- "bi:ʕ-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "boosh-w"] <- "bo:ʃ"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "djuu3-w"] <- "d͡ʒu:ʕ"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "djuu3-v"] <- "d͡ʒu:ʕ-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "noo3-w"] <- "no:ʕ"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "noo3-v"] <- "no:ʕ-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "beet-w"] <- "be:t"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "daas-w"] <- "da:s"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "diin-w"] <- "di:n"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "doom-w"] <- "do:m"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "doom-v"] <- "do:m-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "beet-w"] <- "be:t"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "maat-w"] <- "ma:t"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "maat-v"] <- "ma:t-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "mooz-w"] <- "mo:z"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "muun-w"] <- "mu:n"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "muus-w"] <- "mu:s"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "naam-w"] <- "na:m"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "naam-v"] <- "na:m-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "noom-w"] <- "no:m"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "saam-w"] <- "sa:m"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "saam-v"] <- "sa:m-v"
ord.reLabelNasWord$x[ord.reLabelNasWord$x == "beet-w"] <- "be:t"
ord.reLabelNasWord$x <- as.factor(ord.reLabelNasWord$x)

# reordering
ord.reLabelNasWord <- ord.reLabelNasWord %>% arrange(`fullCLMMNasSlope$ranef[1:60]`)

ord.reLabelNasWord$x <- as.vector(ord.reLabelNasWord$x) #get rid of factors
ord.reLabelNasWord$x = factor(ord.reLabelNasWord$x,ord.reLabelNasWord$x) #add ordered factors back


ord.reLabelNasSubj <- ord.reLabelNasSubj %>% arrange(`fullCLMMNasSlope$ranef[1:60]`)

ord.reLabelNasSubj$x <- as.vector(ord.reLabelNasSubj$x) #get rid of factors
ord.reLabelNasSubj$x = factor(ord.reLabelNasSubj$x,ord.reLabelNasSubj$x) #add ordered factors back


ord.reLabelNasRater <- ord.reLabelNasRater %>% arrange(`fullCLMMNasSlope$ranef[1:60]`)

ord.reLabelNasRater$x <- as.vector(ord.reLabelNasRater$x) #get rid of factors
ord.reLabelNasRater$x = factor(ord.reLabelNasRater$x,ord.reLabelNasRater$x) #add ordered factors back

```


##### Figures

###### Subjects

```{r}
ciNasSubj <- subRanefNasSubj + qnorm(0.975) * sqrt(as.numeric(subcondVarNasSubj)) %o% c(-1, 1)
ord.reNasSubj <- order(subRanefNasSubj)
ciNasSubj <- ciNasSubj[order(subRanefNasSubj),]
plot(subRanefNasSubj[ord.reNasSubj], 1:9, axes=FALSE, xlim=range(ciNasSubj),
     xlab="Subject variation", ylab="",cex=2,cex.lab=2,cex.main=2,
     main="BLUPs Subject Nas",cex.axis=1.5)
axis(1,cex.lab=2,cex.axis=1.5)
axis(2,at=1:9, labels = levels(ord.reLabelNasSubj$x), las=2,cex.lab=2,cex.axis=1.5)
for(i in 1:9) segments(ciNasSubj[i,1], i, ciNasSubj[i, 2],i)
abline(v = 0, lty=2)
```
As with the ratings of VQ, some subjects were producing sounds that were judged as more or less nasal. It is interesting to note that subject p02 was judged as being the one contributing the most to the percept of nasalisation and to the percept of tense voice quality seen above; the same pattern applies to subject p06 who is contributing the least to the percept of nasalisation and tense voice quality. 

###### Raters
```{r}

ciNasRater <- subRanefNasRater + qnorm(0.975) * sqrt(as.numeric(subcondVarNasRater)) %o% c(-1, 1)
ord.reNasRater <- order(subRanefNasRater)
ciNasRater <- ciNasRater[order(subRanefNasRater),]
plot(subRanefNasRater[ord.reNasRater], 1:6, axes=FALSE, xlim=range(ciNasRater),
     xlab="Rater variation", ylab="",cex=2,cex.lab=2,cex.main=2,
     main="BLUPs Rater Nas",cex.axis=1.5)
axis(1,cex.lab=2,cex.axis=1.5)
axis(2,at=1:6, labels = levels(ord.reLabelNasRater$x), las=2,cex.lab=2,cex.axis=1.5)
for(i in 1:6) segments(ciNasRater[i,1], i, ciNasRater[i, 2],i)
abline(v = 0, lty=2)

```

With respect to random effects related to the rater, there is a clear clustering with four rater being more favorable to rating "nasality" more often that the remaining two. Raters R01 and R02 (first and second authors, respectively) are equally postively providing ratings. associated with the percept of nasality. 

###### Items
```{r fig.width=9, fig.height=5}

ciNasWord <- subRanefNasWord + qnorm(0.975) * sqrt(as.numeric(subcondVarNasWord)) %o% c(-1, 1)
ord.reNasWord <- order(subRanefNasWord)
ciNasWord <- ciNasWord[order(subRanefNasWord),]
par(oma = c(0, 1.5, 0, 0))
plot(1:45,subRanefNasWord[ord.reNasWord], axes=FALSE, ylim=range(ciNasWord),
       ylab="Item variation", xlab="",cex=2,cex.lab=2,cex.main=2,
     main="BLUPs Item Nas",cex.axis=1.5)
axis(2,cex.lab=2,cex.axis=1.5)
axis(1,at=1:45, labels = levels(ord.reLabelNasWord$x), las=2,cex.lab=2,cex.axis=1.5)
for(i in 1:45) segments(i, ciNasWord[i,1], i, ciNasWord[i, 2])
abline(h = 0, lty=2)
```


 As above, various items were rated as more nasal than others. What is interesting to note is that all Items that were rated as more nasal contained a [+back] and +[high] vowels, and the only word that was significantly rated as not contributing to nasality is the one containing /ʕ-ʕ/. 